{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from dataset import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.load('data/data_125.npz', allow_pickle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "(x_train, x_test, y_train, y_test) = data['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(175, 240, 240, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(44, 240, 240, 3)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import backend as K\n",
    "from keras import layers as LL\n",
    "from keras.regularizers import l1,l2,Regularizer\n",
    "from keras.engine.topology import Layer\n",
    "from keras.engine.saving import load_weights_from_hdf5_group_by_name\n",
    "from keras.layers.advanced_activations import LeakyReLU\n",
    "from keras.layers.merge import Maximum,Concatenate as Concat\n",
    "from keras.applications import resnet50\n",
    "from keras.applications.resnet50 import preprocess_input\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Model\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping,ModelCheckpoint,CSVLogger\n",
    "from keras.losses import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_t = preprocess_input(x_train)\n",
    "x_ts = preprocess_input(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_t = y_train\n",
    "y_ts = y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\envs\\environment\\lib\\site-packages\\keras_applications\\resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
      "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
     ]
    }
   ],
   "source": [
    "layers = resnet50.ResNet50(include_top=False,weights='imagenet',input_shape=(240, 240, 3))\n",
    "layers.trainable = False\n",
    "for layer in layers.layers[-20:]:\n",
    "  layer.trainable = True\n",
    "x = layers.output\n",
    "x = LL.Flatten(name='rgb_flatten')(x)\n",
    "x = LL.core.Dense(1024,activation='relu')(x)\n",
    "x = LL.core.Dense(512, activation='relu', activity_regularizer=l2(0.01))(x)\n",
    "output = LL.core.Dense(1)(x)\n",
    "model = Model(inputs=layers.input,outputs=output)\n",
    "\n",
    "model.compile(optimizer=Adam(lr=0.0001), loss=mean_squared_error, metrics=['mse'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_weights('model_weighs/model_125_final_backup.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "175/175 [==============================] - 85s 487ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[3.9597423226492747, 0.14506183564662933]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(x_t, y_t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.0751123]], dtype=float32)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(np.expand_dims(x_ts[4], axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x216ec3201c8>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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s+p0DQDqREcdJ+ygUjja2O0ASbSGCcUiEEUJFfNnXeoWq2xYnMxXmqnawttvUoT4kSjtcxwOh42QvZk2mzVF1VCswP1ETL03xadQM6mvbygUOBQQAo1bFcFcSNYnTjPUxZ6lKczH8ckePXcosaoa/AJpWKmfceM17PCOnLBGp36qywmhpaQylm6jQ7LTMZArVFFvpx8UzytdqfoypKuAPBcCNdNH42054su0wtESJpJf1ZJzWfkmlIvdv2ccllTXIS57aDwHUtVDjGjBWjbWPdTJG2kz93CootXW8uQkAcvQhj65vSyI8qmom85bIsK/D+CBAgMHYhCHOkTiYRHCOskeVKG7miZ2b7KuQ8qdEeYIUzJSYU/4ptAVoZ7aYqaKW64HMk3C8AcdsNComMmc6DUbUSXa2lQA2zlTSYVtU6c9R2eDGc1NmlXWg5T8fypOZ1PGl+VT7yc/KWHZEibkp+WZUctrz9SKSpWUF0QIyrBlToGr5tsQzv9I1day5LNGnzxQo+ax0mxoTQPtlSksw1pycUGSu64cCy8SEv8yK0UGAQGDGxWYNNUd1olOIJwOTRKl1zqEjJ1omQQI9DMOpFMuTsQwaSasESbcyRBhvs2S6nrZl34LWk4u3dlyyDoqH6wvbKst1jGz7VG9Do4gEsiE0Ow0ZIQSEuENN+jEgDAGeQjxKS1c1cjk5hj+PBwFg5+CcS6c+p/Gxmk2ik9KYZVJzpyR6GlpC+btsL1cdM9I6CMWBFGz7znRrsWSb7SAwhSaPaYAUgUtHrFLEVPJHPcEjn+T6kfDKtrWlec2lAwGBgLPVCnYQdfI7R3Cug3OEznXoXQenyzWRuaQ/o7RJ3+vOodHkKJ19XA1Y/rLXPKwcibsCsw6bHb68PLcbBYVaWDkS6vPreHQtZQQCg0OAD954ykOqISDAe48QPDwHOcYbIYXiMgNd50AUJzypZufQdR26roPjDuy6ti2PtsqsjleL19pUZiO9Z7srzZrkdG4F35CZjNXTRT82VfsKcFP5FZiMvlsZVJXLyEJPiZOl2TieFRAkgViS1EwHAQIcAs7PzqCsoBPeRanhnIdzDqEL8DTAKSORiyaDE5laqbCjeioVrK06UUZ4zQcjhJpl7n59Ll/xhEEhDYLamqwyUzsMzX27NpKdpnZdXybz4D0G7yM6BQSWiT/4AcHLGnrSEkKQ797L2roBbzXruq5Dv1hi0S+xWAjzu7hTLm/jNY0x/bN7DAYBW7dQVxOs0n7apt7lVUPWzi/qNyhmvubYmDEvp/iM+NuRgy5p1yYeIb6TgxkBYVYYHQYIMOC9N1c6cYSFkDJwEIZzzqGLEsc5hw5d1KIInXFQWY9rCi5pJLESZpjGSITJNxTFHHl5czvD2rX2Ogil8BzDFQOfridbPSRzKfnuRpTpv5ykl0qOEl+UlQI8PIYwYPBDqseHAew9PA/iE4hqcIgrO8KkARIQQ2D28IHhPQFw6DoH7wf4foD3Sxwtj9H1fYz8DWmDUTYlduvLsrGlel+Oh12vz3mke8ffLxX81XymjHqNOdNd6T+Jmyocq1aY7SmAQhBQ9+yxiaA9lQ4CBEBAt3BI2q8T40cOr8gSyrFDCB4EhvMAh4gTgdG5DqyHSVgpHpHWqmOpWnVM1VIzTYyWfTAuQ4qwEqPcaFImNhOwLWVqp5ytp8yzTaup623lyYaIlulZACAED+99NAuG+EaokB2AadJnWrpOJjwz0mnDagczewQmDH4DgBECY7FYoO97dF0PhgPiiz84hhQ3JxUjS3ud9CNpO9MTjQmfE6XP8tbYyC990a26q0lMjHLLtaGpcWFkbtQKCmfY1qVnjqAcgphnQxiwGYZKyJbpIECAEF/PBOTeoDxpAyR0U8J5xC8cggcRIzABwcEtAKCLBzJSmx9sTxf+gJqePDF2kuqjMspnxuvA4yea5aaJnnfalerhHEWWjl1UWakrhAA/ePnzHt5vEIIHI/oHFCyK4B4FKsSuL7k1MTLlNvkwyIEp7MG8BOCALr+sI68o1GTasZ0w3EcXGpNuovOoKLOegKqpjJ8a93mjeq6yN/CgDRFyQ0+KUv+VOpZtBKGOj5hzAgCD38CHAwcBIHY+574ZJ2V8sf09ezCF7BwhBvolHHWiQCcAZtOr2e7MNn6LYdSRs83JlMuwW5nbUlu/z0vtKbtUPezTkss+X3OY/laJb2nXT1GBONr1fthgGFbwvInSPyTJwxxMr7U4fEyfbU46pJMJngbQ4EA0pOXg4Ny4lNh20dTaUrekw/YHRtda1/MSYFnWWMrXoG7rnwDnCaFkHdUujgODi7eTjR6q2mK/618IASF4hDCAg5+TGIcDArooqoIcjHwiC0fpLDgB70W6BCNh197jKDCOlkdgYnToMnra9sfvk6a7AYZsm2a1q04E6yvIqumE1rdT2tUW3S3flAkQqWXI5GcvauTg4TcbDOs1vN+A4eMRZwElelSdSrZsSp0yplGdVGI2gEWrGzYrEAKwWMBhgc4ROJ6zbaPvSlMtawslnrcm/i49ZbQa6Pv9ssTN/oU9B3bLMNUwmhh9InNtFaRvEaQFAMSc4xCAwKDG6dCaDgYEqPiel1TUI+rSCc8cAzgyCjIzvPfy9lgGFv1CJnDcmprjbVxGzrTE6EZSgaHMmxnBbmvOjrXI60lFDdmirAbRyudavUjrz7NeaiRJ2DI3ymfK36326bcod0QtDwHeb7Ben8sKQPAAfNxPUU18bsMiqYqVHHBZQygckPoiUFbNwsMHAnmlsEenEsGeG1+1oOzLWC+VZpNZ6I91z6qcpgYFhLFvpjTtCuIK+pouDfOIxMPwCKSmSOOibksPxGfjRfoPfoAPsprDPG0KAAcEAprmPOWp6SG/MFKBgZlxsboAB0Z3s4P3Hg4OHWi05lqUzfm3FFcz3OjJBATynDEtZp5JcyOpIqV00vX1VHVV1hQATNWXP7eZJgFR08fgB6xWK6xj4FZU/qFgoS9NyNO7hgEyVdb3Mi0CAHHyx3kbmOB4gPdmsoV4bLqaZQq29nCBOKGT9syILxS1WhylfAzIa+9LfGqkYG7WGceyu2znXKrLoInx3lKMqc46B33wGIZBfC6hOhp+Ih0cCNQTP12XmwCMdKvU/c1mjeA9uoUD4wg990DXpfPnHGdGTJM/f0HMVtRqJ3xJTQvizQAXNnBLPTX2eVKPS1W6lbI6PFV+I3CkIAay1ApxtHJkFEbA2dkZzs5OAfJR/WWZMUmy59LEG12zvLbbMnoJ6kB+6QZDJqzYsIDXoSSC8w7sOoAcyCUXmAEARVGCPUiD4ylFCTQqahLBoaJ1C+jnNKVWt8a41sZsj5X57cKmmkstiV+Wh6RFyuTfYOPlL7CPwVvbgeDgQACopVXjfXqq8kUU1AALZsZ6fYEXJ4w37vToXAcfAhCj1uoNvFr2qJPI3q8u1jQaZkNyLQmDZu21mi5Gi8gAgII6apmehi7Nw0WxtZSBYT65nkNfrRPJY71Z4fz8DBfnZzg66sXkMibAtDe9TBasS1qtRieimCAhtoHlMA3RAnoQPAZs0KGXYKIQIj3aR+ZArsr8yfWW/dBMmmfCt2LLuEzMQC6nMfGtaVO8iHTO+dk2EQNLBOd6WAsABI/kyP1F1ASmEnM1iSqbVCPS/GaD89Mz3Dq+JevPFWrKe+EoCi27aSUCi1PbtZo8E04xhhO3brzMqqKmXJS0ZNOaXEYlNFM4dChvZyHCDfNmQjNJfVdJL0tPtMkvVhd4cfIC69U5los7IJIgLPXJ7BxAY1XzOcM2Mr8wu76dx4MDwUOWhIPzIH0JX4h1m6XjVBhLZWmEqq4t620k1QrMUmZ6JGLEXLOvElykz9kyiCDBcUkLGU/8xNOBwcGLQ9fLH4cAe35+1i7a6WBAIG873SWNVV0C4sYUh7AZcHp2iq7rcePGDSRl0uzScboboyhPY+TtunxdZ00hR+qFqfPOuKn8pSRPrUgbVkhfHVhwc3OZaqsZ0Ko3Sx6GbHTxYFysz/H87Dn8eoU3XrsFJpe0mVTshIlTRjiWn2zyj6WYxho4iZoLBCYPB8jmpMBwxKNJkiuoJKv+jGqULveNgdT2mv0SBUGz77aZYbvfa6XW7tOKOFNunNwc93Eww0dtIHgBAIL6b6aCxHI6IBAAphiYKgZMJ6ygvO4coe87+MHh9OQUx0c3cOPmDSTvne1UM7HKfd+Jk3LeEUujkcem+U7X8U5znFw1kWrzoSSLkqbRqHeboDZ9IF552RNwdnGKzfpcHncMaCSgFll74et6yxY2rjenVqTHQ8KMKar/wMCEDl4iQXl8tFzBEqp91ZMU2dbO64gtKnJhtoixIJhOVzEX5svVb5Y3dXyEBzwHDPBYb8QXIFGbkUd+UcwBQubzCesMNT7nvskSxpHsXuv6Duv1OXwYSrtZtb6oogPayaWKX5oBrQM9tOpQ5R93ejMopbDdqboWr9eSN01IpT0ZGyj0PUewoTzjJS19SjeeBHg/4OTFC4TAuHXrZnzfYBhjSnJUbAE5+6lmW0ua2z6IkzgDE2MIKyzYoaMOmUuyllRPPMECo7snk3Es3cki8cz7XZLisWWSX9YkmOOP2l+lX3VFBZC+8AjRIbgCgeHcftP6IEDgOpNzDn0vTCOeUT27ECAoM9VwQ6PfqhlYEKnVbaviTq8jj1PtzQfqwRbfRfZ+Zyq34/q8BFCtR5fnAMD7gPPzC4AZN46PJXYCpUZKhvPG79GrVVml1eSzgNmIk1CHH5gjiBFcYAzM6MzmouR0TStEpVnUBHWKzlq9X5w8xWWnUj2O5cTeJll3kbwtQZF9D5R+T402Qw9pjk7BMMD7AYEDiMW/IhrMboB0ECBQKzrFNI3/cLqbLqZvdu1d14cZBO+BYQiiHhGgp9OoRxko+KdN20SGetLum8pn1A9hd/cpM8b8QBk2bxNhMkKxTVspXZgZq9Uafe/Q9wukgzcrE6yocAc4srkL/SBJsSynOam4DmDZRBTgJfoNDFAAkcR9INIs9nvlIK7NKJjJ3rLwyq85rigBfA5s2pbmtIFtmoJiWQ6Nrp/Xz+wxDvoqtfhbhd7kitpEOggQALTzjWGmAN3Skgs1OT5COT85h+VyAee00/opO6NNS5TAlqS5vK2B3UUjKMuoNQ5Liy14qpDttI4fkD9mMQn6xRKuc01/S40Bc6clZ4jNU7S1q5LrH5TbGzgg8IAFh7gxRtX9EUtMljn1PdPPFTPtD+ZXTer9J9P2NhkNB18cQsfxrcc+LvvuOauvBAJE9FMALyCngA7M/DtEdA/APwbwLQA/BfC3mPnJbgWmglEP79bYAf0kAB3h6GgZzQJzDHbFt9OImV1z5dZkq3a2Dy7ZJbXCg2fVvyLf/urm+B6DqykiZzV0WCwWQNhMltUCt9bqQJ3q6/OtCKLJBZ8pjdpOGp/aghuVP15Cru/X7bhsageDbdcglRK5jkoQzGtwAJLZ5UiO3QMg0Z+BwW5e87Bp91feTqf/gJn/HWb+nfj79wH8ETO/D+CP4u+9kp0kdk10W1JTwJGTfepdf8nJmhnosiyyje6RD2BnppmvbyeQSNI5quIs5zI46lKf7VO3JoI1x+bpnKQtTnYiQt91homNluhivvQfqr/GpDR/8VQEyUd6nZKpqN81f85jTuvhqboRyzbPzNCpRhDHtidriRptIpMvNkpiJ8QXxixhw8FECe7CEy/DHPhdAH89fv+HAP4FgL93nRVsQzciQtfLEWQphlxtPSPY59R4o1tgHz37OiSLLeelLD1ZsyMyUN916Do3ctpNpV3ospORY4XJfJstXB28S3RdDxcPGiGm8gDQREtVb601V1pDCfDmZnI+RvODyHyXcm2b8hl/7TYUQB//LZ2MuWo2z4Gqe7ZBNXvCAC9z2i+g8QE/C02AAfzvRPSviOi78drbzPx5JP5zAF+7Yh0p2U0hk3mig63ve/R9h04PujRe6Zoppsrepb59UxkZRtdex87lRbOAiLBcLnD79m0sl0uJnrvGZKesToRpmvRPjkvruw6ui31kytu374jzXy7EElfLaPtw5pvrTvuN+7jf7FKicw6LxVLiKgKn9zhOtsukq2oC/z4zf0ZEXwPwfxDRD8FlSWYAACAASURBVHd9MILGdwHg7a+9dbnaG32oE77vO/R9HxmIVIuUNCGOrkuKj2naT71+WXQUiSjplV23wL2790AIs2fR7ZoS0CHtpEArpqJ0PlJ8wkFf/um6Dp0Tbc5tPTy0lcq6Uq/WpBRg0FYrto7ITirOVdLYT2ZBxHUOR8sjDINEDXLHIIfGcu44XUkTYObP4ucDAP8EwF8B8CURvROJfAfAg4lnv8fMv8PMv/PG6683y59FeirzOLLHlDs5iTVqBS4eXiFmFSVtYS7tJGF2lETWLpuSYIVz7RIawl75U985EHVwrsedO3dwfHwD0TpuStzJ5a3qNwPJbh6nUp12rBAd/6LklaPKHZInXwiSuuYF2wTdEWQ4/zl24lmPf3JdAiZlAyXL90bZQAQ7lletI/DYDmnQtcu1uh75Dhj7QDuweH55dIS+6+MGogEJ7Rqar02XBgEiukVEd/Q7gP8IwL8B8M8A/F7M9nsA/ulOBQYGBS5Ut/oP8b5jmIGzDOvgug6u7+C6BYg6AE7i4MnF5rqicyfatkv7d2rWPulnVSbBgGZguMC4c/MWbh7dQB9BgahLGtSuibGLFmNUeyZITIBMeIFrh0W3wLI/wsIt0KGHbvjKQL5rouKv/m8kGEyLizoavNhBTrfOgBk/lVcvo8wFgIJ56RLs+GUwI3KNk5mBvuuxPFpisVwCroMcybs9XcUceBvAP4kE9AD+J2b+34jo+wD+gIj+LoCPAPzNbQVZoNo2wFlzy4eCZJVfVgZ6F48sTy8c5wgC8dE4eFMBP9scKirZrzpptwV0TC3D7eM0HC/xAYCsCMi5iCJ1jxYLEDNWYAxefNpyUJMH+HI8bWotvidJGqW+ArjQ16OPINAnp2B8btQ4IAUONPsiHUdVCOltK0b1/aleTu2oHX72vnGGNkOEBQJlWc9eZS6cg7lOLasqgxzYMRb9An6xQX2a0BynXBoEmPlDAL/duP4IwN+4bLmtNMfsRIBLAEDo41tuSN9VZmnTZ0p6m/VNTfK5JZcpOvfNv2/abxm0NkOiAeBEi+oXCwAM7+O7IQlAcb7gXGl5FSCBOmkMREPCOoIeMa5vMO46JysCTqTd6PVsbCad1cAn/DvjLh7b1nW6Dt9NMltgwaFC05gp93PMRwDXh57E8loeCnLR5AWwWC4QwhGGYVVpTdO8djARg5dNpN7b6EhyXQcXnUmFrQk7JvNeHAsA20JBawm934TMz03VUefZVn+dLD2jeoiiNsBgyEk+rl+gj5NLfHkk5i585Mlq7/tEva326JJa/qVmQFRxncOyX2DRRRMgqP5N21VEtl9b/WDuczkTt43XPA9Ulccym0AzWrvE+HVjBNhQ6Ly3gcdlVNqNcw6LfgEsGR0Bg/ewL0SdSr+QIFA6qYSx9JVlOTxVGEicX/F3HByGdH6ymArVCiMAqL/PJTvh9pX0l40NuHQUISPa5ADBoXOifjM5kUxrAOmlFdKfrWLaqx9zbRCG5ejAFQDosVwssegX8tJZUASAAFBXziseT3StLVTXpyL05J4WmsGpNsGYbZtb/Vy/pASgiX5qPd40cVD6FTQPGw0LEN+Bvps5/g92Dv1yIedTbNbyohfmCXNJ0sGAwGUYX9CP8ksunWoBzrzNGCh6NtUjgy/aQWnPxZgxNFXYHWm7ar59ythX+0h5KZo3kPcGEoAQCHDA0snKAdYr8KDSSf0s83VR8jer86pUS4lkjDrXAY7QUY9Fd4Rlv4hAXju9VBqWqnHZKAMMWwZMQ4/1u7Wzx6Hc9rloPmk1VJodej1/Xg7UAd7pWVk6RRwZ6WSOkYPoegAkh+x4iSKcSgcDAvskZo6Sv0PXSVCQi0xL0ftfH9QhabfBSGM/ZT42nEB6Wfgrmx/XY/W/pCQOFQilEotL0RHonMNiCWEsIrBX9XyAdoz1PaupkH+3Wi4A4JzY/J3r4TqH3vVY9L0EBiHSs8/EyUredgCAtauj0WxPrSkiiiYkupa1n9X3UpOOSAcALkfKuo7gh+EX4w1ErTRl4xIRuq7Hol+i67v0ZuK0bJL+rb1Ftf01Y4Obp7eNtUqgtOe9Vg+3PD8q7xq5a76s6E+xU0OXoTrEQJ2l9O2mg/cDhgHgsAFA8T2R5XwtSyvv6ZuK+65H18sqgHPx7dJd1Npepkk0ka+268Uk5EryG4Cj8RjPg8XuGt+U72kX7UD73kUfCzlI7MOC4Hw3ScPBggAzp5coOqfLRw5dt5BXXPe9bHZxeWXAblfN/VQZ/GUlaIHFnFOwORBGDRzblNtBpE7RUR5VdSV9blJwC/JGNLcrC9DVVIm5oLI0AhYLOWfADx4XF+c4v5CXWljpoj4ZAPGgS0RpHwN+iEHUoXM9FosjLJdLOQHHHoRgVTB2aQzHw7Y/UNi+bN5vXUy2vR3/dtnpEasNVWZabdE0k2olrnymWEGgOE5xeVS9W+K/kMwSiBXPiWSCm4kcPBgQYCs+WCWxOI0W/UKkfdzo4lwnk99ltV+XBAvvM0WG5sxkTNsDYPZx6s05D6c0ma1lVp/yvc34HFGm1ISzHT0yiAqAMwhFKsEJHDmQQfHFH/FlHq7HkSN0iwUCyzHlq9UK6/UaYbOKr7+S1151vUz246MjLBaL9Kf+m67rIa9dbwOxtn9vcyo1i6dPO96lmC2gsXOiBsDM0JVMFTJjBMRt1ObR+osKNPV7xUodUXxN+XQ6CBAgclgeHSdJShylYVL9u7QU6IiidFFVlgByaXKrg4SK3q+k9i4TcYcJfZk0JTUuW0ZxPZVb4F7ceJdeODbKr8+YX/kauaQukyP0/RJ9vwRzwGJxhKOjDbwf4p++tETGQKT+Ap1zEvjTdXBdjgtQ0DE1Zp7Wiy2bzPhcuCrhOhJP/tizHOZ0IIomimMxVStvqZzHmUzhRkNA1kS28dlhgIBTEMiReCLlMwdk4Itef31PYME0hnln1PBd7LC5/FPX9k2teAT5ceWiJcVmxZfylLzTsHVT1RS/xX3XyrgcAqiLphkTFs5hSceyUQWyhVU3/MjLL7Idmz4jACQiVO01Yr/U1RTox81j82dfTVIfKDKbGoJi+njSHYprxAKUZ7O1AaAdb1Cmwt9STG5jUrWe+0UAAQBwXdecnC6t58s9p8NNlG0hKy2uTyD8TNJlA4ts2kmjSPb+FDpaX3+cVpSBQFWKXJPYm+k/joeT+CAOwDiFsjTKtBZ2s1xEHXqb75pl3DkHG1TX2TEVGStzxKpUNUVqn+/tl6AtebcDABpLwdZkaD63A30HAQKE6FmvCM4g4KCn1VqbX2wkhrODyJdD8H+rUuSJStGWuTxi/vIxti9lSeXolmBnL0efC2djlQBwiOYcQcW8eNqjpB6BQBL/hiYrlcvb9jebT5jfdcz+KBUakKlrlxWGvbW0GLlqMeYaTEJbzi4kzdVzECAAInRmeW0cLKKOP2s3icNqhOKpyMufAXjZlINwfg4wZJpKyPOYCGMumTQpbZ9mZxTMmNRRaznuUie5i5W7ArC1XA0dToDToKFJYjWpufoclWOBwCBH+Sr6q4/SNv9MMi645snMy5fBgZ2UXmqB7DgdBAiIJuByhxjiW9Mpd9p1HJE4QdNVQEQbYlXYkTq7b9m2HxomgZGJpe+pkuppVpTqf6WDZalqS4mqxFhbppyBaIKps3lQ0qj9or6eCR/MqDQU6nFb0lkEpNwHUaDkoaHi93R5e6SsmKKts8B8378ubX9LNVHHbJFvJh0ECEiaYp4yj6YpR0o7jn3/dNXn9ZyDBGycx0s8xubCTpqDERkt2pIWz0WWok+LPss78dqgMtUw85ptq1bXoDNZFo2+teRyS4Gx98QUMb+ncrJ9qs03+4Z57wwQRSMsyJStuwyvSTO2+AKU3i1lHRAIjFNrQtsVg1ba1/M/l7LNJRPWHjqhA2mXfKhicDsv8vzlkjFLsT2bkqrccvWb72XsQk2ZZqNSEG9L0SywBkBJVZ25/OW2jEPRgp21sNI8lGfnSq6ennM0Vktre28mYyVM0Xj7I9uK28chUa42zT930CAwaW/t2RlTg7mL1sDRAx4Gj466pGrVmv7ks6OyKQGK/GpMoQkNIT9Tnge6kwciohIHs2NO6zPBJVvL4DqevuHVLy2Q4nurr8rnyeygU7NlDCqx5mRKqMalPqXL7ABtpcs+q1pg+nENqaYkaSZJmEw/N0fCwYDAlhWgPdP19j77cCnTjXmHNk3cp+qemhRKgjO3aYfmWuWjOMACUTPZwSRhzT+bisLN1xlzz7aTS4RS7avYIkyA3QasKj6z7E9IxTZIbav0tgNrLco+U7Vvn3TNgDAqc25YttR5MCCwKwDsvoyTB/uyaM5xFuuS17YDPPIW1XLEC1Ogfr4xgLmavIlFM7aMgHR1Jy21mvSs0jPH3mXEq23niXEaXTPlFOCZfRAj/ajZBGteUXF/l0057WCsEs3H6+z1d9PLvN28bNI11dgdUr0MuHfgWvQVzW2WOxgQAKulmeOcp3dUlSlFtO042fNhES2pUNUHVC+8EMZuAYJOJv3eYq5xsEdbRa6bsquvY2oNWiawuYeyYqU3q9QlHVSrJlWi1Jj4Ky4lMpCcpEWjmDNjUnpkXIeqPObe3DCHMHe+QgnCV10AGJXeABqq78dbxctep8YShmI2AF4XPEtU6rXJLIcDAgDKZs+1sp5Iym6X07mm4quTFNF/m+rl/G8tNsy81KOecFNl1hN0KmX7WDhuPv9Y4junry+fr4dqwoE8602pbImK3yWbXEt1la6B4jtjmp5dtL18SAiDOcTfdcdsFyIZPEoNYfz8nKZi7hbOgy1+qthHtIfA2yUdDgjQDlw3mZSDajCw965A2q6Os8ZzRFk6KTkjR9eOHqS57uGSq2adlpnR2uVMaSZT9acen5rE1TVuXJtK18jrsby6wF0qaPFTaSrsy2PWIkij3/avNh7O5pHkawuoXdPhgMBLSRLTPurcGbtq2jTYc5CNwzbFCWAMAPWEvdxe+bKcHZ+68gTba5UG4x5MGkJMtUS/csDOTlRd4qnC97N/GSqupKzqxrYHtX7VChoCal99+IBA4JohvyrXdvauXt6R/V4IgOmulqhHs5pO+dCHrZ7cHVLLdm75EFqRcFP562e3ayWXn6zbHKz1PRtG3spzmW3f82bVLu254iAa08gCAmOCMCP9ty9po3DGbmvNQYBAez29tr922245TjR6Lkve3RyPUBLMpymtzKYTvqYzWStjh1H9/FSyy6j5c1x/omyHydxKU/3c9rRjso7rXK+/jmQdyJavxkn6bTewUoCqXCNbQHIuNHpOTpTBZuNnWRUD9bkqr0+UBxwICAAtdVEluO2SbUz082UyYF6dt/e2O930Gb0Qf8fuuJqizBUt+5sgc864q2gGc+XuDiLzps52ky/z265VXiu+zapp7ctGvoxBhDB7ytLL24GzT5oNVDEDgrlpPomdM2rfzxE0aP6vcLZF+sPl/JOjdFWGrSP8DjOx+dstqSN3XM7kE5eqZ1vaX9elRApFJjHWwFbqDkIT2JmNdGlpT0kztWbcchped2pJ2eKtc/sQsIMXaV+V+2oq+jwN+57T+PIdga10FSfN7hrLbD8Xdvwl+sBohwyGngWT7m1JBwECzPk11u2NJtu0gMNLU7EHrXzAeAJsm5wtW3svH8c1pH2i58Y01sFaM7rgZBtKX5KWs2uTsx2/bZzUNJ0Hq0vvBpwoYx/+qRPtccrqQYBAkTjLyclO2CXP9RFUrblnT/V1TrCtZR0QAl6+3XqG5HYrdL/JcDkekBWC7Oys4zXq2ItdU4s35pyMrBGpFCuNDrJCkHAUhbXbQr9rESNhsp3egwOBum2XzWOHjSbvTJUwlWd/Zptm4LreOl91yAVa6D72Yv+8067Lf7ulsbt4ul7snNeWb4FgN46bo+Hq41CcA9ka7qk04zDclg4GBLTNjJmht0tkU3lSXmrA4Hhyj/m0pGJaGdnOGPPruYAFl3FAU6avxeB548tuknWXtEvo7XWVdX0pWsJx2eSQAFHT1n4FsvT/WRBUpcMAAeMR1yQOwPi9EoncyFOnZOrx6OqWZNRQlHXtN0Tx6clHjPrZ1DY5+UqI5fg1pUAdQCV0XleaXqnZxc8xn2d3H8JuiSbW53d32O1T78sAGNXwuCWQ1ASAjAo5mowTGBW6R1ceBghMpEtLk0LKcjWti0wmjXuujskuyYmDZjTJzCO6ZMPZxtM7umTD44IJxkYFwIFTBBhTSExQrqtXp+zT9Dl9OYs9jcDk1aXaiRfW7Lr7sr3Tc5cJlAPC2o7EdlmlCVISPw5sGtc5S9EeKxeX5VcdP+EnLoGNyzw1uRKcVgHDnmQcBghY8Na52OpzmpNTKGapvp/ACts0MTHup7Lj7V2CvpOOiicJ4JBNFIaJujBRilwyRx11Vzh+SEHCPGf6gr3SHoEgOo+U7+WaMkt+MAGHU5szGIyqITK/GCz5oZL33kZt1u3IJdi0q4NverKOr5eOWhTg0HbY7jYr5mitx6sVbHXVZAE4abIFEfuVtytdhwECMO0d83BO29pkmMNOwnSroX63ENY6idm8xy1fNgp5CLnYoi6jJFi1zjINGyTXck2bbb6R30ADW9gcBsJcLTWXqgoFSm0faSOpRQouuavkL9vd9on0ZCGlLzcJW/mmYjzknr123WbRzz5tc0/Wma/rYPuDAYFmavZGebHJcqpStvbwU7TB6jVsajAct5kwq29clVPmC+m6ARJfZXJW/iIBQ6LLks5UcYq+zCMTEDXKJCYpahcYTeCG5FOwsoAItVsBe8BouY+hBrg2G7cn9Hhvx9xzk31D4/7fJe3rJ3hZDs9Re4BRlxTaAQNMfC1AcDAgsF3Ic8pX7qWuM/JsYRzGb6rTydxa29Vr9nNyw5OaA4wY/GRUitFYifQcgUJV/+gsvInsRJQCrWROmWeJECIkERMolKptq732Wiu1AoGY/dYyx+XtKv2332vlmz2k4xrSSJva0fyZej49W+drPowRL18mbQUBIvoHAP4zAA+Y+bfitXsA/jGAbwH4KYC/xcxPSFr93wH4TwGcAfgvmfn/2YmSbf1VSKeJjlYnWlK9s2qeslA1kRig9Fw9KEGLzQ46dfYhl53oCXq/Frz29L6QJmiW/kmXLpscAvTV69v6h5kB5xpYY88OzCkrD+PjuCal4FTlDtJVZqUylWvtIiD9pvLiSIyPaFDNpkFLA1+ny2mltoJy9TRVdbLe2m2eAo+kHUQbLcmZWTfZfJnAbprA/wjgvwfwj8y13wfwR8z894no9+PvvwfgPwHwfvz7qwD+h/h5pbSPZGBwOplW2cZqEailuGHA0BiU2otfA4WCgmooVq23yzlsgGMcGm2ccYUzr8SFOmIxDb6uCITcchcPDmUA8Pp2H4NMZuKHEDIQUM5Fpg9GTGQnZQQ9MoDItX+iUIqqY8Szq6LsFU6301199XxBClC+lNaOt8nTujYtiXdHAdO06voE3zKKgd1Flqf+YdMP+xQwk7aCADP/n0T0rery7wL46/H7PwTwLyAg8LsA/hFLz/5LInqDiN5h5s93qAdAiVi72GWjQTU3SpUcaWKqjW5tUZH0XCBriJO7Viey+gvoK7ilfpdRPoRi4ieGoHhGvqlK7xPUURjvsUCYOgytT8AeM+6Q30NQLFMmmjk+K0Dg9HmTRf0i4lyMzkHTdjJiXl42Op5o9Xel3VJCsa1anXbCaKSneJzGgBRMrrIuNn2cYSBdi+XUriNHKLfezkl0zmMnAGUQjTn5hex5qRbULE01/a2UZdZugVG75LmsT+BtndjM/DkRfS1e/waAj02+T+K1EQgQ0XcBfBcA3vn61/cmoNVRtZSupbD9Xkv5gCx5dGCL++am9doHDoYYX9RpkVtT5o/GqbhEcvimJZ9ZjjtPqwMWLPPs0adc5LJgLwLpWXEAGjlnRaRB0JEkTG+FnpeR2Q9hYMJoLQXrK8owkmNylzQXh8CxW4A4uRVs4yzkor8YrcYE08/alskNXgkIUAKUVddTmSavPgvDIiIFJlpt6Z5Pu/p1NF23Y7BVW5NuZv4egO8BwG/9xb+4tW2tCdUos/hsXxtrEaVmkfMHDiNQKdbwMT3ZbXnFrcq80EQRAJrg5hnOuURbihzT+xEQyBFCEuhl2Tb5apIzM7rkSDRSviImBQt5BaZ0oyhr52S0DVc8N2aj4kpdBUVQrc0M+0gEXSLKq0ZTpDbKT2M/lQc5zyQdE/VdZyTiZRyglwWBL1XNJ6J3ADyI1z8B8J7J900An12yjlHaqXnjuY65ZSv7WFo3YB4NeG3r53xIT7a0EGXQnKzo1fcBqpJcp2x3W7ucQ6kKqv9AfZalQKF00S6NJukYZ+Lk5ElqtmVsBSxrm+zGyC3bXMHAljIqbW74rCTdxiT7z5GSsF3Kbzo4LlHvzyhddufJPwPwe/H77wH4p+b6f0GS/j0Az3bxB9g0twS3S/5RCGW8Ovd8/AI1uIIpM5XNyGG8nOP663z5XkAIoQg2kgoCwCGWFcxfo85SeTZ/FtgifHEA+zAq0wJUKOoRGkTNZzmbzvwFMALl64HE4RoAMDE8GB4BHrEf4n8BAQH2dzwrgstWTI2QUgxVSsbetu1j2byJrMFdn+Ddn44tz12V/y+Tdlki/J8hTsA3iegTAP8tgL8P4A+I6O8C+AjA34zZ/xCyPPhjyBLhf3Vpyq6QZpe7orOmAI4JR6NeaHaymaiTA5DswsiB9bu2GHEpMarz1hkWpbqYAbEwK4lbtCJKe81D+cUeUrYDEUw8ga2w0wLabckZCzNBNBKHvErCqe7s6OT87gWyz00n9cFQam1FxYy5M0k5V/1/yXJ+Vqm5KrMl/2XSLqsDf2fi1t9o5GUA//WlKNkjza4aXFJSJAkUf80BiUrVbWUm6VdIbUl1EJDkGW8QkpUMqp6xqxZVWcnZVC7tCRBIHYF1JSI/wPAJjOyJlQUt6nDQ75onLsrqpE8+vjpQCPn17tmYIHP/gFKFslsn2C6myLYqr3Efwj7pYCIGryvVtnnLH2DHayQdTP5aA+B4P2sAVd1bBrC8XdFURSeOQaJ8/nJSi5NWkF/rbYlAOTEjphLJpA8hKvgcEHyIcQwuSXsBAJeAwDmXCk1nHnAuuHgfH6N452NJdgnuP5NpcplK1IF4Be3iKtrIFIhs48tfHBDYGyG5+tzWGbJvHwWAjOtPMn1KSmSNvfBPSJbIJbXUyL67QpLroOqmnBCs9Fa1O9ffCtEttQEGEYPZqS+yoCHTacpgRmAJKPI+xHsBPnicX1zg9PwM69UKTMDy+Biv3bqNWzduou97pD0TrE5IF0G0VS+nnZ9yOdkNsQi2V203j8a1FSrcmly2364jEWgckXrFVNP4MkyWwwSBUSdaxjGSu7ChZ1R9O4Eban7+zEuCBJFOdjmJ7Yy2k8WignXNxzDi2vNOltkJsiNZtXDWx1Vq1xqBBYDcL9tUydIsKEFm/HyOCWAIAAyD7JYc/Aar1QVOT07x2cMv8OXDBzg9OQGD8drrr+Odt97G1996G3fv38ON/ij7AFhOQGLIi0CZGY5c7F/p9xDqdyAQnHNQJUKWUQkt8Mo9i1F/S7sveepQ7u5YUKPeKBl4l1eOl66dsswrppfmE/i5pUrcpr05bPo/qpRpPs70QX0refxV9bcRfpy9/6lSIGsKyQlWmgxpfKNXHRAvux0cN5p4pgrOjrRK1qX8ypHMJQC0zIuirea6SuhgFuRciM8QJ+BLHv4QEIKHI4dnz5/iR3/6p/jpT36Cp2dPETDAdR3gBnz17BP85OMPcPe1t/Dt99/Hd771Pm7fuiWrJAw41wkQRAXBRzAK3iN4WU1BXMt3Tj77vkfnuhilCBA6ZGTJukHdB+KupKoX2+jRmjxkTRNqgGwCa1NrJZiukrabltMa7r5gd1ggMDeJpzolcFpJsoO8CyaWgT8ci7PLNLEkBQQu8whd3pQXqYjMYPSPRJ3Mr+jzHkmV6MyLar5KYio0AooOPhQPj7unnvi1eaHtjm9OVmch8j31r+iS4hcPvsQPPvgBfvjhB9hsBnC3BneDtCwMIu2xxPnDczw7e4gOhF//lV/HjeNjOAQQPAguakgBnj2GYcDF6gKr1QohMBaLJY6PjnG0PEJHDt4PcOTguh5d32XnYgQLdZuqP4KhPg8FZPmzXaaAMSe4bSyGZOekHeagDNPLRqrPTkKe+L5jehmOw8MCAZSDhYJZd0hx0m511HLOqxM9PWWdgWmiAyq+spTW1XCxkwvHHuVyvG7bTV43qkkQZ1rprkRGES/1Owdd4lOVvulcaDV3VhWuzI5kOpUxD2cXZ/jxhz/Ghx99iDN/Fqtag3mDECRqQIDLI4QLXDx/jn/9J/8aK7/Br33rWzh2Cyz6Ho4I64s1nj5/iifPH+Li/BxnF+c4PTvBer2CoyVef/0uvvnuN/HOm+9g0S0wYEDnBwTuQZC+IufgqEs8QkRwFKI2IzOfSbQB7dGaB3aWl1x9mp/XPyV/9ungQEDT7Fr/XJ40s1SamTHUCc76XfIWPgMti0VaQe1zMOCCBNwgINAAHzx82IhUZMZi0SFwgB82GDYbDN7DB0bXdThaHuNoeQMuSqxMKyAHEUQbuJiM6igQc4U6QN+aJJ9Kt8rwebYeawFjNTqvGjBC8CAQ1usVPvr0I3z8+cc4PX8BtwwYNgN8WIHDJmpGXp6meKYAEz76/Ke42Jzj4bMvcffmXXRdh2G9wtnJCV6cvsD56inAAZsw4PT8BN4PIFpi8fgmHjz9Ar/5zb+AX/nmr+DG8TGCB4YwwFEXTYUO5HoBA+dA7MAdw0XnI0j7g9JR7YWDlIFtR3IUfcSZP+y+1KzMXd0OuKyj8qrOwoMFgam0zQGY/sszf+bZ0nzIqi8DEGam3iEAGNYDXpw9w8npC5yen2A1nGEY3QJgsAAAIABJREFUBqw3F9hsNgAzbiw7HB0fwRFhdXGO09MTbIYAJkLvjnDz6A5u376N1++8jju3X8PR0RIukNjEUI1AACEPbAQI5qSiikZKKBV4oDwhlCa+79Kfsf8iGJyfneInH/0ET148gSePIawxBA/PGzBtRFtQU0a9nCAgBHz+6FM8PX2Mm4vbIAL8Zg0/bAAK6LoN+k4k9savETiAnMfFZoUXXzzD+dkK/fES7339XSypBw8endPVDY5Y79DBwbkOYAc5PzGq+8kzUPXGnpMmmRXmd8v5eJn0MtT71urIXPqFAIG9gihaOpr6kYhMuK6q+kb1j7avLG8TLi5WePTkKR6/eIoXz57h+cUjnK9OsVmfw4cL+OAx+DWYBzjn0Dvg+PgYR0c92A84OzvHevDY+AE8OHR0A8vlEW4d38Zrd97A/Xv38NYbX8PtW7fR90s4dvDBx6g/l2zdom22SQzZPJS0FcMAUB9GXA5MAEFgno8WT2AIxmazxrPnz/Dk6SOs1hcILsCHAYEHcBgQMIBZwoqTzzSNW0Bghr8YcHp+KrEDJMenO8fosAF81DriCoELHtR14ODw1dMH+OCjD3B81OMb978e/RayU9NBNDUX9PwIBtjBub7w6AcX4DQIirLDr5DkDVAg43QsnNFXTNe9LHkd6eBAIAee2otjAGCT317LKpu5tjPaBnQdYQgDnj1/is+++ByfPPoUD54+xOnpCQKdAy6go4COBoAZgTbJWeUJWJ2fwF0AnRPPvycxHYIjDH6N01OPJ88duofHuP3gNt6+/w5eu/M6bhzdwut37uL+/fvoWKSoTGoXtxeLxBTHoa6ZxR4oVhuyA1FUYnGdFXb/Dn4EucVYb1Z4+vwJzlfn8Cy7BTwP8DwgsC+0gKC4k4pnBApwJKsCiCBATjYgUdhENTsAUYKT8+i4g6MF1mGNT774GO/cfwtv3/saNn6DJS9AnXhkiB0QXIQQRGTwYhJou1i28bpIk2pSttmt+cix/aXnj+uf6Xk2l3dJ16UB7Cv1W+lgQMB2rmqiLZYtfDRk8IEq+9/8jXi+dvAo5zpgvV7hwZMH+MlnP8Gnn36K5+unuOAVGB5wA0ABgUUVFnby0NfAOq+bigIcANc5YULWE3/WYuIvOgze49GLEzx+8RhEHXo6wltvvou//Jf+Et65+3Uc9QtZMuMhOsNcQXcGupaJQ+XdkWOwZtkG60ZJuPFrvDh9gZXfwDsgIMBjAw+ftgrp4S2y1m9JcgC8AIGTvQkyIcWfQS5GH4YIAkQgdvDM6Byh5zVOz5/jdHWGDQ/YhAEU5Lwk53p0cDK7474Hjr6JABanoWlXioiGevvHTa4T191kH7OSJitjv5DpIEBgJK0jAmh0ucbH5Dycsunv4p5xChb1aJ4E3XHyUwAT4HnAx198jB/+5Ef48vnnWK1WGLBCoA2YApjF9id4MA1RXYyrA4wYayC/PYCQXiQQVeUoGQkO6DowMTZeJvnJ6hSPP3qBxydP8Nf+3b+GX3n3PXEEDh4OLA5F3Rxk+yqtElxXMuYRCOvNgNPzM9ktSLJzMLD+lT2cnK3xcghhZNLI4R7R1PF22TRCV4iONwrwfAGQx8afYz2sZDNVALwP0dQYQCy+AO2TEADnOJ7Ei6hpZIlSx3QQ6gClqV6puKmZtRXgdT1pH0m/r1ZwECBgUxnYohdNBirnwFQZU1lYDdcIHEyQ5TdmfPLlZ/jRT/8Unz/8DBfuDAN7MK/E7gUDGAD2AAV4kiUxFz3NGvonIJAddKq6c2Rs5rx3b+CADQ8IAwAs4EH44tHn+L/+1b/E4+fP8P777+ONxS1wCNiEDTrqjKqbD88i7ZiKM1vM3VouLH9GtVdMbATHIvV5APMAho9bkCHmkI2TCAwOJNoAGBqURERxFyHgOkogT6TPWqM7YLFc4OaNG1h0Cxwf30DoNvjqxUO8dvs27nR3AACeAyh0CMQglhgFhw6Ai3UFcOx7+z6HwOYqIx/bPTNvt4Ufx9am3ptLc/6ty64KXNW0ODgQmExRPbXx+PYmFeBQZpjcWAGI6k7ikPvq6QM8evEQ5/4Mq3AR1fENCIOYG24AyCcvuF1eI0A848oOFS26f1+cW0FaEr1ogQM4EIAOriM8OXmM//dH/wZfPn6Ab3/z1/Du17+OW8c3gUEUcJeYVl851rJrfaRq3gmoQTf5NyAedTk7cUkdjvsjOCY5Hp1sABGP+5VUE8uOyNR+5MNIyNzTbnKuQ9eRhBOzRCquLs7xyScf4asvH2O5WODtu+/iG2+/h7fefBOLfiH9TZxOZdLAHmaOaoVL41E6TWP9jb6zCuMuKZW/y1ysu+sKWsM2MPm3YnWgkFqMCQCQm5dBQwJAThj+/OIEz8+fYU0rhI4xDBv4EOAwxAkhEW+g9EoRYbZYjjAdp1tQWiutRLSUYF4zHlcrggfREL3bA07On2D92SnW5yd4cvoYv/rue3jrtfvouh7sxQ8h8QbOlGOPCDPSv/jV7j/tkahUS18Hxo2jm7j/xj105OTQko4A1hUGiQ2Q+V75HNJ4uPI6Qex4IrENOMtQJlkVCczYbDxC9OZdrDzAL0DkcHp2gUcnz/D152/j19/9Nbzx2uvonYvArMCn5peTcSMXadYTmOx5jbqtueUoGPda5slpfhv3d84/aaJiP0C4zm3HBwUCTWld+Aqutz4iwPWEgQc8evYQz0+fYu0vwBjAEAcgU4AcIMroQhCHlo5VQHJISoH6ySUe2BQCmByYZF9/3p/kAfYIgeD6DTongUdfPfkCp+sTnJw9x6+88x7uvnEXt5a3cLQ8xqLrQZ7A7GVSRgAgYzIU8e1cbxqSO8mOpWgmIcRQYsLR0THefPMt3L93H6enJ7jYrIDexToBDuXkymuERrsAgbu41bgDXCeViwWlgKGnLDN8CAAGeHQSCeg8nJMNRyeb5zh9fI4nJ49wfn6Bv/D+d/DW62+CopuSYj+QtisRoaCt4cYaexj7h+upm8HEgmTW7EobVfwYrQFPupC5xbj8oV6W3utJBwMCnBiHs9RHtufmgzPIMHdktlRuuX6uZef4co/Tixf48qvP8OLFU2zWF3HtP4inOQzQrTbUcVaw4+SnkO3eqZlv48uC+RaiDMoRi3HlITAYDh11CC7g7GKDjz47w5NnX+He3Xu4e/sebt64hVs3buHu8V3cuHkTy34JB5cmEiBLicxdJohg2F61hcj8ZGWh1SIYt2/fxrd//Tfw/PETfPrwBEEDHll3BMZnjAKQ4+4dXOdAXYDrJD6AdL0uqvJZa2DFVaE/+DSu7DqwYxA26F3A6XqDP/3pnyF0Dt1vdvjanXuxDH0LSpa8pBqIleAsYUWOXFJQDGIgA8B4QGsHdapHlKTq2Vy14EzFI2YINHgdyvMzLK9h42NzzNBpmzszfw4DBBgIPsS2K9KiWH81Y5fSLirRyD4yYrzvepyuTvH5l5/hq4dfYr1ZAU7O6gtBl7uCmAOxQ4VnrBe46OkMPmmi2FDl9nXbEbJkxrGwACaPDg5rv8GTF2d4fvoVPnVH6FyH2zfu4Ov3von79+7j7mv38NrN13Dj+Eb0eHuj5sYowxEAGLMhHUsG5WQA4tBb9Au89+438eRbj3C6OsWXpw/yEWUcNYDEtaT2EYiArnPoFw5ugfS+gmDOXUwTR80pVSbEzS995AjgAATAhxXIdyD0WIVzfPTZT3F7eYTjX+1x9/U34qnvAbpdOTv98phZnhHtIQJ7Ooq9nNzaY3kRWrWsUn23AJjaZO9bpDHvEeSyGOnCKU0SmeMU3Li6pwe/WuVmbpocBAgwGMOwBoAiLFbj7O3OtkJ9xXiSlw7C+bToO5w/P8OXD7/AyfkJBr+BD142xLD4AZLdbxFWecHZG9VLNnRZcESLAQ4FBjNauszGIR70qbKdPIgYnh02/gIEwmo4xfn5CT57eIzXbt3DW3ffxdtvfQ337tzH8fI4MrzYyUSdTAxwUpczPUgqfaHGE8FRj94F3Dq+ie+8/214GrD5YI1nT5/Cc4ANPmQzDgDBdYx+ASyWDnBiSnHsN9s3Cqbjdx1w7t9Iu/cDAEZHBHID1v4MXz3+Em/ffRN3778heyxiXmYft5oTOGjkJCdwFMVAgqlEA/EFsHOkgeDkvQG6ssOAHtXW8h4WUahRQ+S6bbGPrIRWLYCUvxjNp8pRanyPRTpoHzcZMaXDAAEOEn8PIyGRt9OKXSjx4S7t83ZG0u2XdKCZGZthjdVwjiGssQkbbPwAHwZw8EAQx9fUkdpFpB6qyH22jG7O3bOTbFRmhn+d/p2uyw9Ch6O81569Rxg2eHEBPH72GJ99+QXufXEf77/3HfzqN7+FWzdvAz5utmWl0QJAl6i2Zxto27KaT3BwuH/vPr7z7e9gQx7/9/e/j4vVCWih5oZVkaW9XU/oF3HCUDG1WoOS204oeCDvCYG8Xp0Z1DMcBWz8GuthhYEHhOi0FbHhYm0ePkhcgnM5ctKORfbR6hHwpjm5Q6TsNL/1tEQdS9sWxDMZxtpCUWRdSVDpnoyCrWmu9FQLl9pXnQ4EBCATj1kkoPe4uLjA6fkp1qs1AIdbd27jtTuv4dbNW6JWspfdY8XyFpXCwyJyygMABOeA07MTnJ6dYDOscHLyHKvhDAwfj+5WMYWkppLjpOpmtZBTnaoxpN2LpGvl4xNvmGQNnhzBsXmTEQMMFzURiE0NQLcsMwHeCxAEBwwU0LkOjtfwm2f47MEJTp9fwPUL/Ma3fgNHrk8Ri4QQu0JO+NHTfuRYLCqEWghxqzTrBCZsNh7sCTcWN3H7+A7ONhuJ4kv9pGAt0ZLLhUT+qQ/AwoQFmTKYMdoHzkXbS27qwa7kKC4qxKVWYlysL3B6cQYfAsgzCB3QSSQi6UoAAd7LvoWkIqNa5lVwMMJHaMyHygY29yEBXM6aUshCBohgQIR6ts6Bwy4nlNVmzZRZrABw8CCAqOYx61aQANcTbt26ieXREufnZ3j48HN89eUXeP32G3jrrbdwfPMGaNGDzXvxck+PJbfwqAyWcwTnejx48DEePX0EWvRwy+hpD2IGdMGL9z6tBnBZWIEvnJgiC/P8jsKypXFC6NJYPOkbXqUHoorqEoOGoGASaSESr7yLk4IIIA90AcSM880ZPvjwA/R9h9/81d+Ag0MYoq+BVMPKO+4oNiSbA3EbMck83KzXePLkMT75+CP8+KOf4qPPPsHzZ88xOAZ38c1ILqBzDAdG33U4Wi6BzoOcxhHbcVI9tzNKhFGt8xyKfaVgG0Ax2jCEAd51oEA4PTvBi7MTbMKARdyExQx0FMOJu1CukpCR4bEvlD+gn6TamFWlk8Iu40e6q9MQSwBVnn/Sg2+IctlN3tDno9yZUuEVALjUJVplBh/Gb9Kq0oGAAKDht9rhXdfF4BGHLtqWfiPReC/OnuJidYabt27j5s2babKQc4bX8lt5SnteBs51hMePH+LBwwe46M9BvZPXe/lsjkRrMo5IhebJ+cCpznQ78ToVP6eSbKhBDBgyPggihGQg1uojg0KIcfgKCGIHrvgcnz78CKv1GU6en+Lb3/42bvTHUNVQTSGKQTm5SXkSOEc4OTvBlw++xKNHX+H58+d49OgxHn71CC9enGC1XmPjAjzJ2FGMoDy+6XB07PQMlMTwrT5IFnGhgxdqAfK5CQY8lU1ihOZ6s8Hp6RlOz85w0x+hg0OnWhsIxD2cgi1zfvOS3IU62KgYYwMIauIgHw/H4LwjkTiVFW2W3MbUwPHxZFpNbRaqB2GSbyqmroO2rNM5xP/84WsCrcTJJ3C0XOBouUAAYxgGDJsB5xcbeL8Bhw1u3LiFrutgo9Tqbq2/BwScnZ/i6fOnWC0vsPFD3IumUX9JpEtIamV2JIehFskAnBkbXTorCJmGA3IO1JP4ImJZSSQA5hTbHI4rajWBAsFxAHmZMAOfgZnw2cM1hg2Dbjj8xju/hteObmHjPXSzTga7yMzGXr64WOHPfvIT/OCHf4Lnz5/K0qUfcDFcxF18AX4YJFyIZV2fOWBxtAB1nWgmWZbqPr/RGNvvI6kaRyrzNxUdrk7TEAI2mzVWqxWOqAdDD3/hZDJmhyjBcbmEKOedZR+UGZUsvVNtWWvgkLWBFM1aq/01J3Llx+Ia3KGwNF41yL0ANQ1rbUFMlpDMS0bAw4df4fMvvmgXhkMFAYP8Mt90uQfoux6OHAZsMPg1zs7P0HU9jo9uJHWWVHIDiD2Ri9YORgA6hyF4nK3Osd5s4oGYXCBroZqxkZYuRqCzFx6CBedcNweOS1yjRsKCjSynxYDiuFPOniVIUCDIfRNrwMBBjt9kmZAEhut6uL7Ds4tn+OGf/QluL2/ixtvvpXV46aIu08G5xGHY4KOP/hw/+MEf48M//3MwB7il7BPYDBus/QYDDeJ9DwTxaAUseofFsoNbENjFiar9CduWsYYkDk+hp626qmRG1gyCLKEuO8Jy0YHiDk8OYtaRE9BwYUBQR55z4jMgQo4cRFq5KFcsNHXpm2qSlA4uU3UzdZ8IA6hJkUfaDFrZMipBAQQE21/1UqT9roIh8rn6cRgBgx/w+OEjfPjjP8Mnn37S6FNJBwYClvHJXFOmkSnsHGF5vIy5OgzDgI1bo0ePrutECus7uusa4igH8uiPe3QLh7CSjTH15Er168UgcWYS9CJLmD4wmEJczsxBSyG+rIOYkV4VjFyFOJVg7DrdYNMhaDhu2p8Qn6FoNwdr4zmwZwSnTBwdihwQiOD5BJsnK3z4yYdYLo7wta+9jW4QcKrtSSLCZrPB48cP8fFHP8Xz509xfNTDB4+1P8fab7AeNtiETZS26jgUAHj9jddw43YH5zaguFkKMV7BRsgVx3xFm1xOFHYynwwgpd4q5on8dgAwBLzxxh289cZd9MwIYSNRg3AIEaAEGB3gHEiwQyYoOdHwKulf8guQjkzT/o50y7FmZT/mo8jSQMtboImifzlv/LItss9bLbRB0kh7MMaSvBQGjDB4PH74CD/+4AOcnp7gnbffxlQ6EBAopZt10iiOBrBBXFfkZWyw8QzqjhJT2VdppVrixPHe4yKcYwhyzBWRxgPIf4FCUl8dktiM5SmteohG9hyTo/jWHbWr5XtgL6AQAUHjHRwIofLyUpSItTc3An7sm7j8ZV46KrFWlI73puARCCB4nF0E/OjHP8SiO8Yb9+/iJh8BIZ4GZNrJQ8DJ86f44rNPMKxWuHPrBlzHOD0/xeoiwLMAwDAMkT6V9h4MoO8d+p7ATg8yCfl1bal9eeBcl2RpHMj6eDRV4aXfVV0XAHYgDuiow5t37+He62/ArwfZaRnrkK3OUdV3BBcEGLOmyOJYJjKqN6W/sXkA4RdtOauIiHEHUcdhY7Wohsjmu2qrrTmuS4QUTL9YPkiCwzxNci1t8fYepy9e4OGDr3B01OPN++/izp3XMJUOBAS2JVUFy0AhTbI+PGDwXZTUoiEk8xEZZhRpLy7Ocb6WN+csFj1W65WsBqRH4r/q4HIsp+I0tAtNHBiefRo41zkslz2YIRuSvIcnSmaHBgZmCnVVQJ7lBBhIk12loFTi8opKCJk2RgQFL85Ccnj67Cm+evgA5+dnuLFcJr9GjhNweP7iBb788gs8ffYEw7CWpT7Sbbny1qEwDAhhSDa3uBUCOueAuDU4L5hxsl64mlDOOXSdizs1tf2Ut2VrW3Q1w+mLVAldT3AEHNECd26/hju3bsnxJZsBgTq4DrIZyXmEIPs0HOIyKMSBrHZ8gBSe+jQxjTP9Tgl8Mn35/YuIJmvak6DnSEgBsIBWiPcRMqIoM+crdLaCP/NnAMWNaBdnp3jx7CkWvcPtN+/h1o0j9IsFptJhgABX7WwkxduGcR1RWTaeEHn0XXT+RClivdNEBATGxXqD1XoDzwB1vUxBzssp6j2nGFPqXAzAddW6tqUjxSXIR0DAej3ECUfoqEO37KJWEOC9xxBUO9BCzD74tFqRQ4ntGrTpGHDyS0R1lHVJjUBR4i4WC9y8dVO0AB8nfxSB3g949OgxHjz4SkDLe6wvLnBxdob1xQXC4AEvr1qXF5OHZCh1zmGxXETyQzZzovjTWIMM5oS+62O8x/9P3pvE2pKkCVrfb+buZ7jzvW+KN0RkREZkVlZmZXWXECzYlMQC0ZsSCxAsgAakYtG9QOoFDRuQWi31Ahq1hNRSoUZQEtC0BBItxAaQEELqBppSAUWNGRlDRrwXb7733eEM7m7Gwkb34+fce1+8TN0UFnpxzzlubm5u9s+TiZcWcskvII0PF/BWexFBiyMAlS442jniwf2H7G/tY5YGKbwl3LZQCBhQyh+0YpK6poz1JdydqxXBq3PZFNKJBgRiZoyk4xIDu1fuczjzIHD/7gaRwaJnRHagT/45uz44XsY84vmQxtDUNct6gRLY2d6iKAWtN4fU3QwicJUm+MAPTbDTQleds7bFWPG6oEKrrr4XLKcgVGWFaVouzs6ZqTOatukcKiLKL54CJf5knIGlXBe0EazHzmedqLYLVhG0LhhVI8qqwljDfLZgPnNeirb1AK80WpRXO3zWXhAXo14tGbIZZ34QD+BYrHEGPFp3drAqNIUtnOOoaV3QkTE8e/6cFy9fslgs0IWhaRYs5jPqxZy29mcL+MpJxodTY0HpgtG4ZDwp0dqd+eOSs4ONwv1VnvMFNcmCJ3B+c9MK0iEA3uMSwsgLUYixTMYj7t9+wP70AG01tnE5Hq0KhUpcMJWyDnGVCkTdDRp2041tXMSEEu/2k2wukCckGS9hRmnHiHc5JkJu+7CSuYqDYVp6cBnmnAsLw0bKSCL9mCEi0NK0DU27BCzV2NVa6LGMwXajiUDnjHrPDfIFWxHOvXsr6MqRW0USDMZYlBb2d/Y42jvk8YsRby5e+2o4EhFMKUFrJ+KK5xIuTybbsA1z71u4o0HSz9waqE1L2y4QURS6YHfXVc0xHseWyyXn5xcuXt4CpqWpG6wRdFGiCu3Skr070v0NuYlBf3TPb0zLF4+/4h/8o/+DX//oR9zaO/IeDmcLePz4MSfHx87Y6ePzrW1pmyUmhFG3rfdchMRdS6GFqip8XIdy62OdmiIGZxQ1rp5g5L6WWJ3IZpPM/fQSiUDg1BalcZWVWkGrgmm1jTaFi4jThsZatDVY/3xl3LlHrkx561QBiyMORnyNwhzg/EykK3GFPIGUMt3bW2O7kYg9b0gSbOQSjPSWfXEqxVDX5LkKaqLbidZLAqmCk0vQkCuI2TeaCKxvA+LTymfPFUlRWl6wQwQm5ZjdrV0m4y2YFVjrdP4gPQQgTKxo/f5dpZJLEAED52ujmO/UhaLQFIX2fm3HVaqqYFzuUBQFZVEg4oyay7plsVj60Oolxriz/JzI6gA2HguOq/SrVMHrk5f8X//37zF7ccGv/eBHPLj/HtNRxXKx4NmzZ8znc/YPdmkaqEYFk+mYplnAApaLxnFi5YyuSgmiFWVZ+bBlF0Lb8WLmqkDUtQ3hTJdOpp1AxEhxwVwuddt4ooxD5iVMyin724dgNG3jRHl3rmky8LrUwOCdcQ9wApIjTiE6WfsIxFCkJbGWLEXa7W6HACSPlY0JS+Ln7h6ZpQVHNc5LchtwMtlq0rOD52Ztay3SgjEt+JOgskf7JKr1D73RRGAt0g391hGv/MLZgRsEpxPXhp3JFtvjbZQtnXVcaZKL0pkIdWbgcfagVFkmimSXUNqu9NINow1iZdM4Lu/6tyitmFQjdqZTqqpyMfk+N7+1lvl8wezigslo5suT+fJlxhnw3FQVy9rSNC0Urkjq6emCP/ijOcvlDGt+zIPbdzh59ZqTk5coXVKWGotQSuHsB2JR5wWt5yjWLrCmpVSFl0ZKNOLCea1ySU04SccEn3oI7baeAIgaKMCR+ey9l0V5jqyAQjRaacbVFgdbRxxsHzg7kEsIwB/f4o2YjV94cTUglTfYZZ4G67mnNSmgSOWqnF0Vw11zWZmhskSqU6iSUS+kMpONEdOtpeMGXE1HXs3m7MJQLgU4giIm7E1wx2b/spibde1GEoGgZw9twlqKKOmasb7mXH+MYJTB0tS1O/zj6A6fPf2Z21tVO5edGyXqfpk2EY2Qeetzh6FrcQLxtzxAJzQV/y9W0TaW09NzmvaEeunj4ZWmGlVMpxOmk222t3YodeHdZt7JaWuMNdTLlrOLOaenpzgJ31XekeWSJ9/8KX+gZhzfecjF+TnL5SlbOzvAAmud+K8LqCYlLRVGbaEKQc1A10vKssT6Y0aTGAx1bTC1cfFR2oXoBpIZcyMkJEelzXNSmBf7vSdG+WShgpJCVYzKitvb99ga7aCMK3fW4iziKLdmUliHFLRRFxdpox6mdSpPHuMYLc6ukO2Pk8Yk4/6JanVUAgl6ftQlIEifYTdtYBkqjrNRNQhSFKs40IcZZ+eyHTtNmiMEu8CmdiOJQFzYy1wGKy0gloHgIuwNHDbMWmF3Z5+H7z3g8csnPH7yNTOzwHjRyVIDNvlrIemU2FQG/RIqO/h6kvTL3B0WuY9oLFAbx61AnAfDurr982XDfHnm3rdtXTaghvG4YntrSlnhuJ2434pin2DOsBaULTAGTpfP+NMvjqmXNTJqMXLB69On/l3ds4wySAHFSDNRI6ZbY3ThIvLOFwsu5gtfi9FA6w8mEXcikyjPoQDazENjidKBKGfALQrn+guMS2tnk1FKM7JjJsUO42qMagvaGrQ/Hk6MBQVtcPu1ITfAxT4YZZ2/1OcSOEOgip4Cx51tB9ZcRWJnaIwcX9L+pL65V2OdWmi8i1Jl9/RyNuhKBo7RhNDuQQiKdq/QwRjj1AJjUVElC5Evm9uNIwKdOOxrtCHubNdQQfH6Xt023Lt9l3/s136D37eKL160LJo5VoSWUGLM5ROErLHE9T0VzirEDL3LFWae+ahD+Ge6rw0EAm/mDCHUyhLVS+8oVJZTAAAgAElEQVT2PLuYc34xp6qEtnUHpfqjeZmUE8bVmKIofTqsO8XXqgY9sqgKrCyYm6V3rzpwbaxxR3mVlkLCLNxalCPFWEqWyyURME1La5yFXkXOb6NxVZTT95VXE5RWFP4IN6XFuyydO7WgZFSMmOgdRrKNO26kBFMg1hdJMRLrDxjj5+oRAKtCwWEX2SwGG128QjiObUW6C25aj/kS7Rld/XyVSSWVJvVL/wKkxGu5lmi7fz0Tp3sMfQQZiPifvAM22Fwk5cAEA80mWLy02qGI/Cci8kxE/iD77d8Tka9F5Pf9v7+QXfu3ReQnIvInIvJPXzb+O2uWeM5gWJho2AlWoE4TTGupihHv3brLxx98xO29O4yLbQoZ49JcQyy7195titLLM7VipKG3zF861Q7weAAxklnzhwlagKdgq0TAKocEBqExsGwN80XDbNEwX7TM5zWLWcPZ6ZI3xwveHC9YnFqYV2gmaDVCKOLBIi2GVpa0ssSwxLIEtUR0ja4MomusLDEsvO7t4gZa29CY2lVnan1AUW1pl5Z24U4xFoRCawqtKLRQakWlCyqtKZVGo10shSgwQrsURnqLabXLSE+p1IRKTSjVCCUF4o8mjyhmHSeUYA/yKvEg+Oc4n+1a2tOO9t35trqfXYRceVAg1nYYOvL4khiA1Z/kwPyTzcshvwvRbsF7DKzN1sKsH+sqksB/CvxHwO/2fv8PrbX/fv6DiPwq8C8APwTuA/+jiHzP2uyEio3Na4/XVAMCJffaGOBFuo5doA8KriKOaVpKKfjgwQcszBL9+ae8OGlp2wmN8ScPhVtNkFK6803fho05G20bVpwRzZjIKdZxmqg94CUD6+sg+kWwvgaBwWBFe5Hb6b3LuWU8nfLerQe8t3+Xg/0jxuMxFxcznjz5mq+ff46xhlFVuCPWfFhrLOYhQeCwYBywGSv+UNYFTeu4Ud3WWHHzcgmR7h2UhqpwbrkAmEq57wqvBrTgRG9FWwNzYbS3w1a1B23hpAZdoVTp4wZcX1H+nxfzQxSAc5DkeyARX8hAYmXfCK61sOD5525zKkL3UldayGwIsk5m7DycBFPrbWO5tBAIlPFxHE6aTEVoLmuXEgFr7f8iIt+5fCgAfgv4u9baBfCZiPwE+MeBf7DxrmjUS0B/VUKQRwNGicrYWH0mp+I2GoqCwQcvJSimo20++c532dva5rOvt/nyxRfM5jPq9g2N87F4n7lN6+43P6fvV9jmlfcL3owQCOTclaqnf6bnhL7uPsnmElQHF1QlSmgstE3D0c4tfu37v8Fv/LlfZ6eYUukSXYx4ffyG+XnLk6dPsRgKVdEua1zglc/KC3ukJKbKW+uSVYxxgUSNbWmNcfkKWmGVom3cicVawLbQLlpaK87AKAqxBbQ+sScgpGgn6iwtqq28LWAH01ofaqxjtmHYRyXOL64LlxCkC9dHCPH/ztAnEftNVBVcqTbvSsRZ+aVHJGzCZPrcOXMeZb/JRgbQBYTsPgbpzGqT9MdmIr/BEeJAABCuhEffxibwl0XkXwb+EfBXrLWvgQfAP8z6fOV/+4W1QAgu7WeD31cIUXeTYsrD2w8Zj8dMtiZ89fVXHJ8LjYwQYMEJbbt0CECIPkwW5nUb3qfmOZAEg2507cTzAzxBMEEgHd7MoFp2H+25nnLRda21fPidj/nxj/48D+8/gnmLWbaoomI8sUymu2g1xZoGoQJz7tNxG8fRTYtVLsNRghXeaqxxB7QYjNP7dYiitVh/dFsIunJFLw1t6422yq+9ApQ4yUUE8QiurWU83mZUTSiLglZZtK8zGQmB4MOva+azC+q6ZlnXjMYj9nf3Odg7dN6A4C1QOuK2D/3wmrqPR4gEIqiP3mgYjJjRgNtF8M0I78O3JXgGVluuk1+VieTNKO+lgXgQzHVGeVsi8LeBv4aDv78G/AfAv8bwWw5Cr4j8NvDbAHfv3M6vbLpt86AA1nr7K37h05ir/lfbuU4jKCm5tXuL8ahiv9rnyavHvDp7wcXFORQtjcwxtDTUKO+TD9Ev1/dmpHmEqLRYesDXJ1xrY8j0RqWSKyt6rsRn2ylHgG7fusPRwRHW10811tlEdFGyu7ePLsbYuqbSI1op3fjGYBtoW0urHLAoK2C1i7hTUBbuWY24Cj+NjzbECKVSlLqgKAuXz+HXX/w+Yaw36onfMQVoH9VZsL2zTVmViHJBSkXhIhPruub8/Ixlu8BaS2OWzBczFosFy7qmrCrO5m+Y1xfsHxwyqcYuoMmPo73qEKSJXKcPyoD4nAKH/oqYNDB41thlKJdLpH7QsIFrYfLycYPs4gqk+sjRnop6lfZWRMBa+zROU+Q/Bv47//Ur4FHW9SHweM0YvwP8DsCvfO/juCJpM1b6d7+vmZsJO6qFcExXdL0NNgeAIoCyWNOiVMHB9iG3Pr7FnTd3+PrZVzx//ozT+YTT82MulucoZjTSYmXpgmQwLiinM0cbJYROTkHnXRIBCPpFgJHLiIoAEutdBEnCRjhTylnQtda0dUu7NDCRqMS2xmX/7e/tszXdYjFfMBpVLGaVNyw1+GhhjIUGGy38Ipqq0Ghd0tqWpW1o2jPssgGcaF4ozagaUVWFswco9+6taTFt0LODWTUl2Bhj0YVma3uLYlQgWhDciUx1s+Dk7A0vXz7nfH7q8hUKi6gGRCFaUZsFL07e8PL4Cfdn99nfO6IqRxS6oNIjH4jkgo+0Lih0iVIFSjQqupZdBSYbIiE9UbDZ+vZ2YnCPulJgMkjk0pvxhEV1FPj143V/kFjDMPhuHNnKVFS7Wc14KyIgIu9Za5/4r/8sEDwHfx/4L0Tkb+IMg58A//vVRt1sFb0KbUtuwRAdtv7Nkw/WZogpIO7462ZhqTHsbh2y9/EB7YcNr46/5qeff8rXz7/ivD1lUc9ZWKG1NaZ1nNGGI7ttfKsICCH2oasaRLztrsXwrBMD8ZzePyHZP1QiNko0ZVFhrWa5rJnNZuzsbkcVyBiDQjMdT7hzeIvXx68wTeM9FRprtZMYrDuEpPUnArm6jwX4zEr8YS3SOjVBtKIoKhdijEOiQhSFKtCFBVE0jWExd+mvDkJbnGffSW9VVbG7u0tVuuIx83rG8etjXp09Z9nM3XP1wp8HYAhplGH/tdLAki+f/Qk/faz9uQMK7UG+UAXjasJ0MuVw74iDg0N2tveYVtvOniA6jqd6xV3c+jqbQ2LuyRu16iaEUHWawLlDUJp42WgNA8/dkwEEyH8S4vmQKB++nMFfLLW4AYEuJQIi8l8CvwncEpGvgH8X+E0R+XN+6M+Bf8O/9P8rIn8P+EOgAf7S1T0Dq0aX67aw6EVRorX2FuPVPvnfzmdvJIyWP2uhsZjWGYwOdm7zw0+2ODy6zU8ff8o3z57Q0PjiHS5gxuXQOXuB4HLY4+tBJAYr6oOkDU8JKLmFI0gKKe012DRc+UHxKoTrppRgGoVmzP3b9/jed7/P3aNb1Isl0mqfmejqHxhjODw4YHZxwcnxKy9etE5bFoeYxrh+wcqufApttKnUQCsoqylUwaSaUhhHBFQr0FhEabQSdFkwqoTxqGWxXLhwaasx0qLQjEZj9vd3GY8rZvNznjx7wus3x07VU4t4kImo2jNXS3CTiA+vdu7LEFMRPCUK4yP4jCqobc1FfcHy+JRXF9+wt33AvdsfsLt7wFiP0WhfxSl4bnzcgV9rwj44IPLBgTZ6BNZx8zxRKgFh+F+G5RkLjwFmATRxUZUh5kNrzXg0xjSG5eKC0t9r0+Br21W8A//iwM9/Z0P/vw789cvGfZct97OKciKqdCzOVzCTrBG9raf2DrlHTKqCB3cqqmrMSKZ8c/wV5/MzajtngStWGmoQmJBbnzWRZAhyp/Cmje9ICDa4d9x1AUTcxgfgCzMMomV0g8WLir2tfX70gx9z6+g21kLbhLBaN7TxOQf7+/u8fP6cb+Zzlq2v04cGNJYWY1yNAay4Qh3GokpnhTbGl9hQQlmUVHpMpUeUSkc41oBqFTRubVSh0CNnL6jrlnrh7SvSMBpVjCdjzuenPHv2nJfHL1ma2iVFqSXizhpD2SauQWjKKowoF3QURfcmEV+v3zfSYFiyWAK1IHPN8flLnp8cc3R4mw/ufcDRzpFzbbSu2pSI9eHFbi9SEEJ4fqpFuKk5br1KCHISMNS6cBwkPs9YRGFVidYloKnbhcskFF8A/V2rAzexWWNQuvT6XfdQkqsZ7Pp9AgcPRivAOjfjSE25d/CAid5m/8UuT559w+uTFywoWdTuQNMGV2cvVBEK4ylRcT+CMJ+nrqbwURV99JEI4EteB4QXIjDFbEmcKG9ay9Zohwd3HvHx+58wLifMFwsHaCaU/4LWH7Syvb3D3v4e5ZOKV29OHJERS2sU1hY4/diFu9rGEQWNq5BsnOHDJTgVmsIfjiqqoEChtTMSupjrFtP4HAZadDGiKKCqGpbLBW1rabjg+M1TXryG09Mz6nYJhWCUIL6qcTCcQtCw05aJtVFUj9GngalafK3BhiYEfIlCKUVtlsybJXPzhraesbj1iFu3blPpLS/ui/cUSHquDSnEV2A0AdKiChG+27iHffAbbJ4RBJuKEjcvpRRVVdE0FfP5wkkLwf6wYbhfeiIQ4qeVUhRFSVmUhKCR6zUPIba/ZJ4IWItLy7VYIxQy5mhvzHQy5nD7Fs+eP+XZ+WNeH7/ibH6KtXNEDEqHGnvOKBRFyTi6d1FFH7+3a2iDalWsqQAh/cSlvCLiC576BBUjUZ0RO6EoKx7deZ8ffPQDtqsdF7vfuhja4EWwxvqUZpdYc+vWbe7dPebJ8+cslnNXpk0JlgJRBqXcITGtdadEmdZ63du9UqE0uigpyyJyXq0LqqpkXFW0TU1ja7DGBRJZizEub6DQJWqsaYwz/h2fnVPX7oTmyMqsoPDeB79aEQbEuvj8vt0sM8jGg17D0WxBjxeDiPMeKL1gWTc8f9nQ1jMazrm995DJeButCmcljepcSPvOJbPwXFZ+6/9+GYRuguFA3EIsBCiULihHwhhDaxvq5QIj1tuO1gcH3ygisGo5Ty2dgtvVclwMeuHz8csYSELmlw3Gt8ARAkrG4BvJPivpPkOCMcff4ePKg/FvXOxw92jM7tYBh2eHnBy85uT8DafzU2bzGfN2xqJe0NQLoKWsKqxtaNqF87/nGUr+/YIP3SgDTettHSrWCBClvVpRoBmhtLA13WVnesBktMWo2GYymnLn8A53Du7CUmiDyc3bD/L3F4SmWTKdbvH+B+9zXs948vgxr45fsawNVeVqGajCWT5sW3s13GKNcW63okCLpSxGlIVLfdbW+fR1UaB0gbGp5r+ze5S0taVe1lhbMxqNkMJSFG5BWmrqxQLTCkLhdGCfHSMiK1avQEDdVqVEIsnf1yb1Key9YH0uvru/QFOrC16dPeH8i1e8mLxmb/eQw/1bHO3cdYTPpmcmDu4IfShvvgrTXWKRKYIB2EjAIAnIxU+034L9R3B2Ue3CqEejCWLhzJximgXGigvFXtNuFBGAK+jvgq8A463A2tkA3N+CJBMmQ9o6W3tg+tYG/XzILx90ywBI0unRGsFKyWSyy4PpmLuH7zFbLrhYXDCbz5jV51zML7i4OKWuL5wO3M6ZLU5ZLBacNxfUrYvBF3H1+pwXoaUxBrEuSlGJs8gr0R4hFFvTHW4d3GdrOmV365DdrUMm4wmVGlPoEi0FYnzwkQrzTnKoDWKlEmzj/u7t7/PDX/kVjvb2+PSLz/jm+XPAorRGU6F0jbRzf2py6wKEimCEFUpdURUlWhVoUSi0y/c3zkBXSgE4Vac1Lct6idYFk61tlFJcLE5Ztg1FqRmPK0QZlnVDa2q3/jagtPLIhreRWFqJoVv+TcNBIcTU8sg8IowE1HWfrK9Aba1h3lzwZgYnr2aMX065fXiX9j3D0cERVTHxJodY/4xon8lmsOoihN4GEOJZAkHI7+//0m9Bw3GSl/UeG001njBFWMzOsE1N+8siCXRaX0ciIb5LjFM+jLTwFXm8y0bizeQcIq2WZZAm2OxfnEMiAMTQviAVhD55+e8CXRRsFRO2prueALkDOxaLc+aLM9qm5XxxyvnshDdnZxSzE+b1ArBo7QJibNtgbINpDQs1o20MpSqYjCZMp9sUakKlCw72b/Hw/kdMp1toKbDGicNO31a+4o2XiDqGKEkvqpz9QeHKdInS7GzvUD0sGU8m7Oz+jGfPn3E+m2EtVKWiKB1ytdRY1XpPjFMdRuWYcTF2kX1S4Ny1yonSxnrEdeG9hdbIFmzvTjm8tc3JyQmffX3B2dmcybSimiqm0y1GpmHZLFgsFtRNi1hBaY21oV6ge6ecj7pf/FmLxnYNtMFqnsduBOnEOm+7P1vJi9sz2uWS9lWDZsxoWnIwGiHGByJLCITKg9MuFfa7/YKOH6/1+w2MEMAzvlOqbqS1Yjxx52MsF3NXfGVNuxlEIEe+nkTUpauuKS3eC+A4ojsJN4jt2ZjhY3+dr2InpIMq669E/78b3JXOM1kfhaZiWhVMxjsoEc5nZxyfvEbbU0bjQ6yCstQxWrBt5rTWnY48P7vAGkupS/Z29rh16zaTatsH4UxQlJg2IJd2te4id1nlKt23SIQhHsKhFKIrRBc82hpzcLDLp59v8ennn3F+ceGCI4OYX2mkdJZ6E9KHpUCpkbdYjzyCCSIFtWmwraHUmq3pFvfv3OHo8IjpzhgrNV+0X1JISWPgYrFk1tZMpxN2dieMqTg7E8z5BU3jah2itdOMIydOlnCBhPj9TQwGQfAHnrjP8YSExuIqKjvDn1ULlCrQZsFZfcLJ/JjJ1jYjveUIjXWHsqagr6s06ycYiHRCgDzIbHPrE4sgmTiE0loxGrtw76ZpBkeAG0IErAXbCqIdVxVftz0ZPcBZn8UHyaSQTyUq6siuXz6wR0OT6EwQBMQ6h046Z6Cvv8Fmr8L66MaORwGT9A7POUbFFncOJ9y9Je6kJHH93G3iKwu5kuT1fIFpDVU5YjKaEPgbPvvOeArnvhWRE4SDT8L6hvVY1/I1cMKNxRrL1vY23/v4u+zt7/DpTz/liy++5NXxMVoX7N6asD0e07aGi/MZ5+cX1PUpth1RKM1ktOXsJ23j4gNEsb895f2HD/jOow+5fXRENSqjDeRo95DxeEprLMpoTGM5PTtnuZiztTXiYPeA7ckWJyeveXN2jpLK2QWsdustPh7PhnfKJMPOXgUgMf68BrdmwZYAPhHH92loKJSwbOe8PnnGp583mEZ4784HjKsJIXkkkADrw8glugzXEAfPMKLZKcT5iURYvJJMEfR9K+SHoQIoVTIaKYrihksCbdtwfHzCeDJmMh778+K8y0mUS15Rme4USkYrD7SSfOP9rDu3KWkh43aIL04ZNOXsYJOruRRX5QR38lEaK1kjgmTgy4e5LBxSYQkhlv1po6qKEijHW4AzDFoJRDJwCeU5vxvDrYnKoMapAUnzzRbBC/RgnIEyHI5iWnc+48UFi3bpJ2MoypL3Hz7i9u0jTs7f8NVXX9OwAOtSWN+8OefN6RmmLhEzptCKWi3YmuxwcLDPe3dvc//Bfd67e4fdnW3KwqVyO7uOAluglLMXeOXfh9Ma5suWxXLGyZtT7t+7w3vvvcfkzSmvj1+7ysiCr4JMhJ34+rnmFvc2+5ere0L0Ntl4ydK2S3xJKaQ55+WrhovTlvnM8r2PP6HwVZ/w5zzgn+MQfH3iUFR5e5JvjBIMjDC7fyW9PIN9B5IeRrD+tGQvpW6IYbgRREDrgp29XYqi8BZ+pzMq7QJ+3FkRye0n2eJ1CUAYMdP1OqGb/c+JSQ9UvhwgCN2Q0NW+GTyJ50QR4ZygF14gJfusin45V3aAkD9TfHy75xE5EIBXi/zT8vOwQv8QjWhJejCWxjQcH7/mxcuXvHhzzOmbN8zbGgqhLEt2pxMOD/bZ2t+hVkvKscIsXSix1oqHjx5SFCUjPWVS7jCdThmrMZPxFrs72+zt7LKzs8N4PEYrhTENbdsgoqjKAgO8fPWa89k5UjjCH6ZsvEtyUdd88+w5R0dHLulJwenZKfO6oW1DaLa3h0SRz79/pnOHwiFhbWPSjfHqnE376yRPMG1Lq2oaUZjGcDF7wejZzzi4tc/9o/fd8K3n7CkcjEubdPe+cxZn4GAdOFslbLG7zQyTkkWmYmM4+VC7GUSg0Owf7GfitWRI7+umKenc0y0Ukvh8f5Eua+FQjCgxJ1lyTUuEKIyQCMLqTSGIxzE3x9nC/LvzXCVCRB7QV42UF/1C2a4+pwnqEvFZEel9DxcG7P7NZhd89fgrHj/+mmevXnA8f+MOQrEtRgtlUbAzmfLy9ISitMznb5jVC4egbUNVVrx37z0ODw7ZGu2wPd5lZ3eHiZpQFiVFUVJIgWkNrTHO6yFCUZYIQlEWLMySJ8++4fTiHF0WuMJqnmCLID59+OTsnNYKt0WYTKcumensnEVdx3UKWyFhST2RtwlM4t+0LxDLdPnrITozNGsNrTRu6YuW16fP+Oxnn7I12mNne9edKt24YqmbLPrhWWGPuqnI1u/gOkDuEfZOSe3AZGyat5dwNpUQuxFEQBAKFaYSFiSExjgqFlS7eE9GBLpG/OBC8upAJiVIzwUYesY1hVgQ0uIq96TRDENnIeYqhEg4sTaL8+7Bgg156nFuQTT1oZ9ZlGAHqP07R/FRUp/A1eI9HQLZF3QcITK2pWlqTo5P+PJnX/KTn33Kq9evqM2SdmSxlfG1AQy1XfLqfMmrsxOUNhS69RKbO668rWtOTs+YTLaZVNBaw2KxwIql0iVlWVHqyhtxPbdWwfbgZrRsWk7PT6nbGj3S8Zg0G//vXqAoSpZNzYtXr9jb2aIoNeVojMEdmuJKzLXkix+jBrMR034kaSioAHGdeuK49ethadGFYdG84fHTzynVlI8+/JjD3UN3JmP3CatAsLblfUN9A+nd7iMEO6qsJDtEhNF8RLtxCjeDCIiLNrPYWERDxB8jnetIoXO80f+1iQ7m/DRxEl8s0iiwpgMGuQs3JuL4e7p9c3E9IK3z57txZIVAQAhtzUbpBG30Z5yuBwkiN4x2X1081ctkhR4ABrGwy1GsP5+g5dXxMX/26Z/x088+5cJc0BYuMKmldtUGbfC6O0AU5cT0VgkixldCNtTtkmcvntEaw2LRsL9Tc7C/z8GkwAo0bYP2BlwofDmwtKTGGpq2cQZBkVgRKZJDk62RrxjV1C2nZ7OoMooolC59kVMbPbrgxOSUO5CtRBT6JBKDJC4IKUYzdPYEQwytXSCiOV8c85PP/5RFu+R773/C/aMHkYaE4CViFmIewCYd2KNHuGNRkwxKwv8SoQr3JbizNnOVBgmI/IbVdiOIABGge0gUxBghnVoddk76G+o1u55fyBJ0JRs5T7xgieJSepAnQiRrcaZsRJE6AmhmN+jbG+JLrHzyHGhFncleXQKgpD7dXilQZkjkCNfcmrj3st7ibzGcn5/x2c8+49OvPue8ntMWNZaWhtZVWhZXdDQSAZu7QpUT1n01ZmuF2ix59uopr4+P2Z7uce/uPd6/9YBbh7eYTqYY2yCARiPeYGb90KK0C4LyhmClBGt8kVdfKjiG/EbkMv6gFX9IjA7RiV4iyA45teTr0yXSqf6me06Hm2R4abMdMMZipUFJA9awMG/44ss/xtRLFAVHt25RqgJrjC9+vymxKAJfb26pIkBKNsomE/clzNPD7gCuS/b/oXZDiABE0bc/V/ExerlNYJPSb7svHai7E+XyRJ2gGjhdMRiEHAx4sFF+YV1ESKZzhDKlFhO5sRla/yQhZFdzLa77XXL7FR0o7NyRfRfiO+XwO9Q7DGxMy5MnT/jiqy95MzvFKhceY2xLa1taab06lLubfMow4BKJsgAbLzobazGNpT6tOZ+fcvL8NR9/+AkffvABuhi5PhgX6OUWGAso0UyrMUd7h5wvT1gwd+XQRWfSsPKxATbmU9jg2bdC62saFIV2ZdCs8vp9WhFrhxAhS7eVkOPvP4vESmNOpXBr4PIQTNxxsRe07QU/e/IZda348Y9/zJ3dIwopCIbYQRtBsH52LgVJlLinkVpme5juy38fwJ8rqCM3hghIYPs21wUTQnes8gMqgRD08GwhorgX8rDDOCFtJ0ld3jWeNtxan5eO8+WH8TJMlyA5iMvXam0y+sEArYrz6hUWWdNtbRvoID2xvyNRRVHWFTxZ1EuePH/K8ZvXKG1ojCsX3tom6sXG9uMgbJQEohst1rHxCyPiyzFYlsbw9NVTGuOIyo8//pHPH7CuTxbcZY2h0CU//NUfYf605YuvP/M6rj8VSIE7DsREQh1Ec5csFl7T0jQm1iIU7c8iMKb3LvnSGEKt1q69KLx1IACO6FiPbO6vL2Ril+hCUzcXPH/5DX/yk5LJ937M7f07NMtmzZ71CXxvXmRgnjH8/gT7NuEVG/EV2s0hAuJ1l6AT99ll7EcXuwJ+Ct690sFSkuzuOWUU1ZMBzQbdVxMTY/Kc/GDBlQyJQ4qwEomAgspdf2FyOeCFOIdgK8heo0cYwpoMfU7fV6UjNQAFIuIrMAu2sRy/OeX07JSlabDanTdgbIurMAymDRzURgSXeJ6Ye46JB7Ok6DtHG1yCldYaSsWLNy8ov6743ke/QlmMkNZGCSBoMsadp879B4+YN3MuZhc8fvolqiq8KtP40u/dZwGJqGc6e9P6SghKe0Ju4np3hQEvD1oyD092tWflj6BkPUE1ziYiQGtaRsrlVszmrzm7OGZvZ9cFwJkgX14mmPf2jdV97+/rlduGrjeGCCBJ58KGDYgXUuvo0p3bfd8AJAEBxVeVISMe/ndJmx/zriX42m3kelH6yCBPPEty1bFc/xCmQRjX/+3GfXlxA50kkaDDZG6j/M0G99omIhYZRs46+lwvIJw1nJydcCPYeHYAACAASURBVLGcY204uyC3jq8SqJh3IOGdLcbH1ntW2HlOlAy0YdnWnM/PODl/w3gyodQ6eU7CnCVIMsKD+/c5m51w8uYlp80Ca8SlMQsEA57fgrilUajIiG9IdY77H1Ko8e9hw+z7YnbYqmD/8CnIXpW0Pqirtf7h4fnWslQ1C3VB3U45PT/lYjZja7LlJcWkeGQLm/ZXctE/f5+uUfPanP4K/W8MEQjibFRtBiqv+I4MiXXxckBmVkWoXNhLixk4vAMJXzUr9u+hg//densBRIO8dVJEmF7UBCW5HFMdwzCLMCch113jxez76uZ7AhCQaINSEZ7hIvKWnF2cs2hrJ5Z7FSFwxBWkjrTUxegHO4ltu3sQputSoQ3GKIw0iLYsmyVPnz5lf2ef8XTb5/OnGSpRGOsKnExGE95/7yEvXzzjD784oza1Ly/uYkWMEfpnAPY9IIG4WX+gC9oHnnlmYG0S/W0HUjpiJQCtcaHF+Ys6QileA1JY5az/rW1Z1Avmixmnp2+YH8zY2dpy0s86oHS76HlXAowgCUv23KE2FCnbH/syQnAjiIAEK3bnxwBYGfVPN2weL/w/GK1I3Do8pW/Rd8Dv6wJivQRno5W+Y3i1eATPkF4EZU0GYCnQpysIriKPExqSN7u7DuveNSMQa3ZZMnZpkagqzJfuEFGUYMSHl1ovHK8BpvRGHuiyY62CbTSupMWfC9giWJpmyfHxaxbLBWxtd+bXT3Rqa8Pu9j4fPPqQn3z9KfWsRQod9yCXmPL9XInu9B+NNa5GpE/FTraTsNb5xmZr6vfRmKD3u0FjXIE3JrlDjo1bW+3sJPP5jDenb1jMZ16A8gRjgABki0vg+onA+5WxKzzh6u2XSRKAnjSQtVzPHQLSYW7PIIdcX3ZMkFi/2xUNNfF+G42G4Q6VZZ8FwBRRMfEkgqhNG5urH905ZbO3XWXHbigG4UclxgysrkB8Z7FpLUxrPBLnxUVW7wsaVW7ddm7G1fdIzpNEKBKSem6axVTgCUCIZgzl1Iy1VFXJzs4e43LC7GKO0OBOmfbzyIqFxIn68UxAcHHaeuT4XoVxlZgSIzD5HubvDrEGgdsW5y1J+5tOiLLWiYQuoU1o2obTs1POZxdhk+KgMZw520R3WEtQC7rX3gbvw7O6DHB9uzFEII+9v+qLCwMvmKsUfhVjrHjveemWPnf2UW0Ezp7ks4DcbWbIS1GCZIeH5KHCw88ZfqPuuBbVuRoBX3r3RSS3JKsq8f3znIKyLBFcPLwtQ5+wXklaSefageeL/p2IBtE4lXVczktB2iO79aXBk+idejq1QCGiKXTFqByjrHZ2Ae0Mky5Jy50nEZ7uhvN2DV/kVSyIMameIiFU2sQj3oIEakK58kw6TOqCRXwRG2cHDesfmEMukXlCYRtmyxmz5QWNbSikAlxOYYjojQTc0UhUALGMIUiODG9DDdbuSbdtCim+QW31TYKqsHpB1q5bP+Mw/9v/F07DcUUrfNZiHDtlOK6MJxKr7Az9c+4rFdOih58dJAcV0mFIs+hKACp+kvh7ijbL39MBdlkU3Lt1h93JVtI7fc1C57YOKdsO0ZVSXp8O2X1u2NxWEuUI6f1D/GlCmt29PYqy8Bb1cGPomLKFHJJrCl0ynW5TlVUMJMLPJ3qQxBkN3aGCNg/IizNL3NW7C1vj6ypav4+yokr0ibW1/nDVXNvoeIH88fU4AtA0S+p6ycV8xmwxj+nqIeagQzzz8Tor+otrN0YSSG2tcL+mp3T6db9l3/sXrjgHT+8JpaqsFzIDAbK92ID4EG9FlrDpUVROMQsBiJLRMPRR2VCXi3Q9CTKTFhwnMdG9aSiKknt37nH76C7PTl7SmplHFO1r2IOIO4/P6jQ/ExXTrCZikMYJ96VnJiIAZTXi9t07jMbjjnqUzzjIvr6aAoUq2dve4+XoJU17AeIiDbVWWJLFPgm9RLXHr2hak+BatO6AOmez8CcZ+8Cl3P3Yb8PqUlIpgifKYMC6MxRqUzOv58zrBdMiREgGa9G7a0Ou5diu+KAbRARWXWHRWr0Re4evdRAjIGwHMLLgos4zA9KlOXT99a4USYgWU6T8/YRsYXO8KO7F7QSIeKTJxf6+SoKvzef80OtiCDqAK923SbKB8YeICIVW7G3v8tH73+H16TFfPPuK1jSIMqQoyjRHvD88FPl0UYT9AhVBBZMOY29bQ1WO2d874GBvl1FZuMM7cskn+vfCwmlsA1qXHB7c5smTp8wujh2RUu5QkaDWRxsfZFJNWOu0JgGGgtrgwpAFaUNR14wHm26wmrUWTC4phOCkXIpRWKucWK98vQef6Jk4fEb4PGMIgag5HexM20/9MlxelVwy9fcKhOAGEYGrN1n51kOgNfp+//chROrnAvT7hEhCAvfOJqN692YPc4aq1kRDn4MjL8tYF9NP4KRhE8XVrotWYoJUIune/N0jcPb/eHXAc/KyqPjwg4+obQtaeP7iGeetommdNV8py3Q6ZTodMRoVtKbl5evXzOYzWtNkATaBWAgiJgkBXp2ghdF4wu72HoXV6NYhjcsklM7sEka7wKaRHnH/9j2+HH/GyVkBVICllRoR7Z4XMCQz4ImPQ0g81yYO7NMDrD/6zNk7XDJV7gbshhpHWQwLMV4h7IUiky5wcUGuVLuKtoVQh1BIKkHY6vhMSGeZpO1L1O4qza4SBGfS2SwC3zgisCqSdZF8WPS5lqyfxrGsRoVtEK/ya766XSc5uasmxLscwFkLWq2M5yezysp8C3aHOO9cngxQOdBCeTFrvEQiPjQahRJLuVXy8Xc+Zmdri2cvnvHs+AUnZ2cY07K9NeXOnTsc7G0zGZfM6wV/9uVn/PSnn/H69CV4z0maRMj+cyqTKwCrqWdwdHDEB/ffpxCN8gVCxR9DnmhZcJCGdQGlCva29nh49yHz5pST5pU7+FSW1NJ4gut08XwegfNa/7+OaijSseu5Y7x85N+AKuAIXcgn8Pvak7ZyXR9AjLi8hbbFmMbPyhPhXBoIN/e2L1/WKAHKar+17fqocPOIgGtudYIObQeq/oSW/P858uVEQ4FN4naOPwidoKR1BCBYyIeuhVq3YbzOPUBMUxPQPeNPsgmQgDB71Y4PW7KL+c8Z64jSQoZchMzJjmTgogT3t/bY397m/fvv8+rkFadnpxhj2Nra8icVjykKYVEvmU52WZ7WLGZzzu0FyvqoOUKBUfGGRFf8Q6EYVxX377zH+w/fp1y6suTxuj8eXHx9geidsCFCzlIVIz768BOksnz+9CecvH5Nay8wUoO0tIFje43RWm/p97/lLtwYZ+DfX4L6mYFVv7hHtIXYrEiL7ROClHTkcFXAWFpraBrjfwnm266w1g16Ei+dBOkobdi6OJCV5uHZZt6hXIJc124cEViJiWe9fz3vk1qH9nfE+UhZ3Q/ujwzc1ht6XdRV36psJL/NQ0z44yl6V4fPiFJuF8jmlGmzBHlSxB/B3ntecAMG7hRuEaU6tMRYi1ZOK7XAdDRlensKt93d7mgyg60NTW3RuuT+4T0+fvgh89kFj0+euSPDrK8tYF2JTC2CFncKshLhwd1HPLj9gOloC9O2IBqRwksCzhUoGYgHIhz3WzS3b92hGDsvwB+f/DHz9pSW0ldX9oemElKaPcPw5c/XRTXmCx+SoSI85EykY1zwYwfjY0Z9Y4p2YbHaYpRFGudRMMa6syQ84UgqU1JXOhQ6jNtN4BxsKYguA6Qe0bAdTjPcbhARGJCN3mHrJ9Y4fY1Yly1wi8GZDUgIHfuCx1Zl+/RWVpywyZrcG8cOZUSE8emIq84KbjqcK7gwwzM6fXNbBYLyyqeI235jWgyuoIir56gRVSK4fH1rWyblhE8++JjGGpqfGl6/es2sBaWbOKZWgmo12hZsb+3y6z/4dR7efYRZWLSUFFpHdSCUOA8Aq4LaFN7V206Wdcvu9gEfffAxp8dnXDw75eJi4W0orhJTJ3gpqBk4d6IT9XN1w6Z/4t2L1jqBRvAJS2HdA0FKVHlYWpSEvEEXEZdU1DYNReFqI4jJjpILckO8tQs5tlN7ZjU7tiMlbnL0X0E1uDFEYNCg9g7HHWqOKUjUHTuSAsRosrgBttcnAp6K3Lqvg5pssyxDNQGjqBBm3B8kO6E4jatsqGw8QNwCV4tib8ZvvciYFoAYt4AnAiljUqGUoTXO1Xe4d4sP79fMZjO+XHzJy9kxc5YoJWjt1mhUFNw9usv3Pv6ED+6+z7gY0RpLoQuUrxMYIgVzUdXG75Y8f8kYi20MW+MdfviDH/Nq8Zo3Zxe07dzZWHx1XUKlZ5WIAJIHNSW/hyM0KSV6Lf/x+xDGGIqSTAupUVYjxhk2p1vuePXRaIRpDVp0iulYgYFsM2zvJ9u93J/bZW3Qi9RrN4YIuLZeGkgvE/pdd9yVEdPvQVTrPU95US+PFuw74frTjYTFf/N+hI6oF/TRzr4E5tQXVeN36XQ1ErhIIlChf7TOe7uDxDBGvErSpTkpDtcffR5kUes+afG2DxHuHNyl+n7Fne3bfPHyCc9PX9E2DePxiOnWlFt7B9w7us3hwQGVVFjrDjtV2WnR7p/z0efidhKLw2JYXBYfKF1xeHiH7334K9SLhsdPf8ZSDGJrRGqUuOPHrc8ANdZHD2YLHfZYWR+N4AWBQfDoFSAJ+2p9rEH8Xchgx536pJTmaP8WD+7dZ297F424WCZJkY6SwUiwLIldga63Rv7OIJfcc8OIwKbWgdpr9O9/Xu0T+FH8NUak9frJqk4fRLqIXXGsbGyb7g83dnTfgdk6tTNlF+bXc1IZxg8A31V7uiqLJZw0lMbpJ9DkRMxiveE0HHRiGRUVtw/usD3Z4c7Dh5zOz6nrmqLQlGXBdDxmXLowWYyA9pGVUS8OdYUCwiuXlk1SBeLbhiIwIQ1YhO88+pB6tmR+PuNVLSxlgTBzh7aISinOxmbnEqZ1iIgVhSEhnRPRb9l9mcTYoaieWWgRtNJo3LmYO5Md9iYHTMoJqpF0dmoH+R1xViTJYEg+GFI9r93WoQA3igisii3fRjW4zr39RKJw/6r+11/9AEnRDDjQFN2pJG6dP8cxfJtJp95mYcJx5p0JDwBLQpwhO0QyTOVA6MXhWG/B/S86Y6KgFGQg69/IsDWeMJpMOOKQtm1d0KwvtCJe1BDtT4gSFY2WQRVJ7lTr6afNiGjCUvfuTt0yrWVabvGdBx/RtpY//uqPeP76OQtrfJ1CQVj6I9Fcem/fsTSszmcG3jXCaHA9uhDmNEdBo3EHrRaqRFNSFhVH+7c52DpAGW/87JybIel+EScB2KHJDbQBIAsFYzqvlb+T9NlIt90YIpAmvU4Jurx173+rWVxJhwp9oUtsui5NMmDJ57euJQMlAekDYGRGpzTmhrE85KeVzNbSJiB0RlHJ8L3nV8/ViAhPPmzXtoixaF8gRQE2loYP800ViRICEIlN+N4XrlbfLBBZwTSW3e0DPvnw+1AIqv0Tvjl+zLJZuPlqUDQYXOCT+DDnRGS63gj3nr7MqPVndAww27iWQZVxFNUXRnVHr2MVWk+4d/c+7919wPZ0i6Z2B6gG9ScaBOPfb8fs3kW7MUTgqsjbtdame9fbCt6eqHRG6RGoPoJnM6Rzikx+ReQKxKXXYi1KSdG8cknsBG6NVivZOOISuHH0zYcv/l6TWcM73KkjIUgMEApekWD4DLcmtcMmYyRBNcmlDz+PeIZfRoj9sxL6OqPldLLDdx99zHS0zdfP7/L01Tccn7xi3oA1BULtBSJXlszQEg+K7aydrxnhIof8gbKXw4pSLsbBnY5dUOgSTMlktMsn3/1Vbh3ejunb4cSoaAq1CTIizb8y83n37QYRgau2DEA7bXjxcuKwmeJel0i87VjrruXv5TEoQQjdgIahNegRHAIwZ+rVoMbZq1+A019thnb5k5wLzR+1pbwKwxoADhpHPj+bVydIgr8Nv0ep3GYZ0ZnnwIovTKTY3jrg+x8f8ujhB3z9zVd89vlPefL6K05O32AadyaBErDS0NqakBrtDigx0QMSDu7oTDt7leRMcbNQSrn6hdodv15KhVYlk2qX+7cf8fDeB0zGW5ilxSUQq1yUiO+ct03IH3Nc8pvyrf2WgsSlREBEHgG/C9zD8aLfsdb+LRE5BP4r4DvA58A/b619LQ7T/hbwF4AL4C9aa3/vetPa/FbDXDitynXFq6v3X1UBrtuG7s0zEENJa4BOPEl4epCfBXp+wzhG0uXdXQm8BpA0vBMB2MKNQRXIiIgNVZe8kS/LHTB07SehWpGbTwLgvso9RNq6U81IVyYFWquwS4PBMNJjPn7/ezy884iffPknfPbZZ3zz4glnzTkiYNQSVI21LdYufPqXW1jTBnWhW7IsR1SJ++6QXytNqSuKsqQqRkz0NlppHtx9xI+++0O2iim2gVQHsvt2qwFx9K4NrMOq8JvN9dtRgatIAg3wV6y1vyciO8D/KSL/A/AXgf/JWvs3ROSvAn8V+LeAfwb4xP/7J4C/7f9ubKFAZ/62a9//UuPh5gXvi+WbgoQu63NlgmCHqP0QJ7dZtq50CIDK7ktjJXdgdjV9XvFFrj7R9cvi54VYXguIlvFwg1vRoCq4H3VPnsgD8Trj+LFjz5UAq94cc8LiOWIazwv8xtAuDUqVfO873+ejhx/x9MVT/vAnf8jjx084W5yhVYlVLVYrnzHZYG3tCFhmwwmqnJuqM2qGtXbIP6Ys3fmKZVWxM93j7u4jHj18n/t3HrK/dQTWFTqVEG7ce6c1tkf/jl0NbP3CbIa966gVlxIBa+0T4In/fCoifwQ8AH4L+E3f7T8D/mccEfgt4Hetm8U/FJF9EXnPjzP8DOi7ZWPr66zdu6T3PXV9F7aWdTr8tSSBIYodg3mIk01cPvXut+Twyqv6rJcsrqVddirkCHaQQHkCYB2CxFsHbA+JMLmQ4v5Br2FtO0+5VC/26kv+fkFUDsFTRcl4VHH/3iN2d3d58/EJL09e8fzVS56/esbJ7DXLeknLAiPKmzlrWm9DUSrUh3DFS1QkdJZCFYyKCqULChlTyZSj3Xv84OMfcXR4m3E1jiEOl7ecxQ19zt/559uuZRMQke8Afx7434C7AbGttU9E5I7v9gD4WXbbV/63tUQgANbQhfVLsF4dgG5Ax2VtY2GGd9E6hCD76o1eyQQgAwiY3Rhvk2tSuaG+Q8C2bkxZ+RZrJIRfEq50xo/+9VB8JVz7FvavXIVw31NYsGksLVDoksOdI/am+9w5vMv79845PXvDizcvePLNNzx7/YzzhUuYauwMoUU0tGYJWEQVhMjG4N4clRN2tvb8iUn73D26z6MH73P38B5lUUELxrYoV32U4HtZqR+YSVXdto57yeDHd9WuTAREZBv4r4F/01r7ZgPSXAXiEJHfBn4b4P69e5euyRDHyB+ZB968bRsS/69CHNaFPDsOtX5Og2NL4u6DYaq2yzcun1zWr7dG68Jgr7OOcezeLZauJDUkseQOl2D46kZR2t5Le0Wkd6ise571qoZPoW7daUTWCiM9Ybq/xe3927y3vMeDw/s8ffWcZ8cvOD4+5vWblzS2oaoK2nZB0yz9GYfOsq99wtP2aId7hw842D/gcPsWR/u32dnZQVl/ak1U2gJhT0FRySwSKX5aqSF+xgDgc00p9IrtSkRAREocAfjPrbX/jf/5aRDzReQ94Jn//SvgUXb7Q+Bxf0xr7e8AvwPwa7/6q2uh7nqI/fMVnS5z8fUliqFowG/7fD8wudTpEM4z4syGsMqcHTLZToRc932uE6zVMxX0CFSuu2d5AtbHLlpc9mF4lx41cQlePXLXOxRlVXjO4zycNCjintc0ThopZMTdW/c5PLzN+7Mzjk9OeP76JecX5662hPInJLcN1risIiWaUTVid2ef20d3OdjfZzraQonGtgHfuwFBjhBcUWLLihxIZgfKX/3n2a7iHRDg7wB/ZK39m9mlvw/8K8Df8H//2+z3vywifxdnEDzZZA+4atucYNQP+9xkelnf3pbKXsc4CF1kXR3Mdx1435gYtEZijCjT5x7x/v5k5MqxCx2u3nnf7toPWmk2bEcMsBp41rCNwAXe4InJ0Nqne9Mx1E6ocKcKGeOyGve29tnb2ufenfeYLecs66VD5rhgrZdWhKqqmIwnVOWY4ORzBYoU4eyErj3nEhE+rombp2zovAm+3kV8wVUkgX8S+JeA/0dEft//9u/gkP/vici/DnwJ/HP+2n+Pcw/+BOci/FevPp1Ngu7bIfa69m3FqisjD4EZhAg9BiXAq27h5kfmAHy1uV1HN78uwIVyWebt4bM/YpQ+nEQ9HPOfAsqEULgjbbfgc4/j11E5ZjyaAM7TkC9MzCAkoLbn7rabCRkHy/4lmSC3YnSJQ3+EMP+OSrBuNYLKGm9a375VFqG19n/dMJt/aqC/Bf7SZeMOPwvARqR5+/b2976r3IU0hv9L9+9qx8vx8TLcC+rzqpjc62czwAyIunFyV2uZtkIYLjr13qmIK92xB667+aQQ7vS7uIIjoTRZzy4hJqUmBxRznj6HwE79CSXoh4zPiQDEDM/evAKdSB4W27mcvoRCOFGTW1mDbu/13ze1GxQxmBus3PcuEm6CHKEnoboU0ZWRA9W0OYteP6O39Rp4wEpCYdL5NvovvyVyBAI2dH5iGN8tbc6NknSy9rYBqccVR1nXP3Br9yHGGYTHrAh8CWQ3SVjuWkBuj0QS9P8eisSlzqWX3ICc745k9oyAxMMIHrI0u4bjYAvoqQFZv3wMRxwSTOTyQeqekr7CtLrYYDv9+2HzWbGz/sAr7QYRgbytiv7XQcY+R1y5s/fj2+hTl94jiclk+3plNeL681l9PmQIGSe1RheJ/QMg95EqO3LLfw8I1bEJiHQAMur3nmeH05876nPPgCoiqXxoICKSfY6x/8bfmnPllEeSE4CU3BX65+/Zfac0MTemzsS4mHwldP6m+/vp3EAeB4KkM1fCH/9K0jP25Lx+CPq7j9kMU5uw54YSgctbnyh0jFY5s10T7PO2aHglJB5a8bVc8x0EJF06mY3KwQCTyLnn8ByHoi43TlkcUMc4+FyMlwzgvd1k7fvbvhkx7XkUMCLhy2AiUro+W4iAsl68z3S6fF4B6cOYibl3+yASJydeAu1Esmb1K/pC/tuDgd/Dzrfh9ktLBH6Z289LGnj7tg5ErgaBOaB6RttFU48nNu+z5kkJkdeskUdEa2yUCLpkbp38lz9hSGyyHfWh+35JEhlu6VzEKBhknD4TJIK1INVV6Ew+l/kHZdisDREzO3j9sl38/x0RSCGnK5aWX9AEul/twG+/DG3FXWi9aiBD7yRR7O8Qgt7659buQULRF+89nK8iZ+ZFyO7tzrlv4xh+VudVIgdPz0iqhUfybD3COYddB2DXHyDZ28oKYqekpmGimEti6UVy5aozpzXtRhABG/9b33IL85DvPP+e+ANdiMo51BpCu2nB3sp2EAfuPa9v9MzMvyJp023vNyAejz3Uko76LijcqjC50U1og0Ewn9BwXyHFKMSdD396NoKrvEnHiObHWCENg3DjoSVD8HVI7cwEOo6RkYOg7sdxxLpaA4Ks1HiEdDhMmEtYD/eo7FyE7hvEicjAurqtD6qRdOe/od0IIgBrXFQBjzuYO9Sx2yxRzSJPY+3c2uMSV2pXe/zqLSvvsdqGdOpNenaO7H2O15/k22se3YdvJAB+Piqbz1BQ06B9ocMP/b3phvV2HUmc1NpVn8g60d5JEp2a3vkrZEjtRfesSIzyon8XUcUXGMmeG85UkPRuucsw/m7DMxNfCCpDb+YkSgSbispclwHcGCJwWct5w7Vw8R2I2hFwrdf+JAlqttcPNksMmzS960Tvub8OGC6jYatW8gEusvLsHPiv1iJhyo13Q/TiEmnisv79iMIw37VjhqvhvjTZzr0JEZMbL1cFVkT/bG6BBEj4HD0CYTwigYl/Cb8Nw8QKEesYX+wqIuTMgNXf1rUbQgRWuVfeIgHoynvu1ysg3rtoOaUG+mEeV2pdFPsWc5Hhz5v6v93y5NB1DeHcb1UKGpJ02voa1cBdc8E7ESlkQ8xDf3o9kixrXjoQgHVSQseHTxcRO+6/nvrRJ0iDngKIjoKwoteBgz5N7oecBDvLdWHrhhCBWHm9++PPEa+vqgLk4nXnaC//q8omGUW7tTI8g2rIL8pTENVbS0yPDd+TerHe0Bb6Bh16uG8aK6gGG209K1zez8PbF3L1QlivNsZAos7FARIiq5F+QUxf6xkgcf3cbpDHJuST6hOAKGX1pIj8GZsiVXMbSR6M7EUuQgxGXo4Nut02tZtBBDykrCvQeZXWX8iNCUfrjKxBZBswIHV/6zx58OOVWs949fMkBjmSR9kqW7Mhu8K3n49TKTpoKV0rfSf4qEeEEsqkDRJsogTidXNZBfrLoj3jtUxE7/ze6ztEBNI7JAkj4WiXRSfDpSOo17FDyRqDa+qygdL+8qgDriVL+CbN+bpj5kYfIS99Jxl8RelRumKX7Y3j+r59sFFnbu9gjHVtc4BPNzJu3eEbV7FRbOrTsVn4tVYQA/5MDBxKhrYwZth+Y01CGOtLgkeROlQV6t6fCEMXhlYyDXKkjX3oIjA5ArsxknSwShC6T8u+RZVluHWuDPDCAKupbsK7wQ+4YUTAtVRO+0qUvPfbJqCNQmAmXmao7Z5pICSLBBh518j6rmwD135uR+Qm+zwsRQ0111/hQnaHpKQ+4fF/PbeNRDhaeqQrtHfmJj7HJ5McMq4Xwpijpd4/cMU5mBG+WBnZsXZPqDJx21cTcgQqeACS+hBOFLTZ/NPUV7n/YOvp8vmLu7l40SIGFCVCF5fuEsBZwYUN/W8MEcgq0RPe0ol0MPgGg3CaFmklKGgNNsfjvLP17WzSqnr87Vpiam9lxLm0xXe9WHTWzgAADrNJREFURBlfd292e/xCtnzxPIOcQmblP4LlK7u306L4LSj/EGXTdG3s4z64VN40kPIImRcn6eQ0qOTJ6b5aIhR5nz5hCcE8KiMmOiMCUSWw6ftl7ToI6Sfr77t06IGH9fbgCu3mEIGBaKghkc5fYugt88CafFPjPdLrc4Um2aPsOsKQ9+8h+WXtOorPiq6eAaLY1d/7Mv4mgFW5CpQRp5y7dQJsvKjvJLckwuf7uCk9eTURqds5BEoJKq27Jw7BPZvr8uvGzolFrtP3pRgTPBNZn7g2ajWnYN1a9n+XzhFvXLrZuSrUvTDwzB7wDN7rie63qifwi2w5w474ZoGOcckD26Zx8rXICILtA/o1KO2mwJ1Ov0FVY0P/q0+hM5cIxD8HaeIyAE92haSJDwFgXo842fKuiDwBgcP7BcFDiGcd+p8j1e9LV9aP0ycWQ8RA+ePS1r3/Wov9FYFoxXjZmXt3zJUWBJXeEnek3Y0PX7/ucJOIQODU2degDoRDL+ASRFwjOERhYABhrsqJ30Y0S8Wt3k0LFu+O/u7f+ZpxPe+oeVIX1z37TlAbnB6+iuRZf9+6HgpwNf/zKkCufzjjoIsEEscTpTBkZzP0EC0QhmBk629SX+TPxKLszXsAcRnVH+ge/na8NAO3dqz/A8Rk7XNlzedeuzFEINfjcq7SdfXYHgUekgX93zXEwPa7WvvzQ6Ak56ZnZi45tekk2SuN78cMvGBAWuzgQcKTzhQBTBsQj0sidHqSyMor9iFvHcdcjX/vd3HP8dWFo/EuwUYo/ZWLu+JFd2WD+rYqOQYRPSbzREmxZ4zuaQEdAyiZNyIPZIDNxICE+HGsHKbXEYMhdaA/aL//FWXEG0EELNCSgEoAfzIkSin66UVqZQGDlLBmYUMbEgN+gRy0X4Bj6PpbtzX7Ha3za9YhiMyhwlDffrUOjCSI4P1CGPlzbIYq+aspGaKPa56RJjyU7y+eMQypE0HHJz7LYpVihckL+beVNjTN3G2YxtpU7OxqbShGY1A9yZ87RBzsAHFa024EEQCLsQ0AEks1BY6Z6Wxk72Mv1YSu1gbFwe7cvl3r398vfDnU7zKw6Y853P+q+uoK8bkiR4vid9yv1ePR4vKG2A9Jn7uEIAFsB70kjx/IJweBCA3igLU9g16QGTKpkiuu0Zp1CMRkLbhI6rjpKZch/Opzs7dYQ0mvA7U3hAjkYrKr9Q5RBXRynQYVDqOkryKtX7ihgJlNLXHO1d+u2zYF0fQ/r8sKuwrHXPfMK0elXXPc/HsS6XNrTq6udfV5iLsLgBKb3ZmfV+y6iBfr12XWDQWWJbthIjTxexDhsRixIArsKmEOksMwwXbX2tbQNA1t21KWJUqrjrpyndZ/ixUpI5uEexVxBlIuQfiwdhvmdCOIgGkN52ezCEwiglIKpTRaK7QqcAdHhEVO+d8JoIbeclVgW2kDkVfdTd+0o1djmV2DVygl1RV1B3hKf5Ts+mYo63s+VqMEL2+ROJkMRSOhtv534w8yMVgTzvDzmrZSESkkTCrOPpf0MsLrXzPQ9ehiHDJ4ZKOttv5aZd8z0T8V+1Dptng1Ra4MEVNrLcYa6rpmuVy648q17jzm2jae/K0ky3jM3qTzipcOMnTjarsRRGC5XPLlZ58DoLWmKAqqqmI0qqiqEVVVUVYlqqyciBckS0W2UV2Kn7fcONZFSEchA4gEgpKTjKshzmVImdk7coo+CFx9BO4/I+OoVwQyN+ZlRCbMCV+W39I1ErQxKMcYQ9O0GNPQtjWLZU1d10CIqHO2nPFkQlWVKBXO9NOIVtHavaLLdwxvK2+Rz/KSd5CN3+MINqkeK5JOLsKvWeJCa9R0wng89oeWdMOx3bOHnrwqnQ6ysI3At3nfczy4DEZuBBHAWmxTe6ZsvDW4pTU1dbNkuRxRlgWj0ZjRaITS7px4V+o5AXiwJucAnxsO+0DXya8n8qaO5riuDRlw1rW+FyDcl3PodUbDIPFcRcxPMeyXTmm4WU8w4rMszsNvMaahaVoWszlnZ2ecn5/R1C2maTBt447gUoJWyv0rNKfnBYUn6uVo4ohCWUULvvEkXGnt7QcCahXFg2bhCNP6l7sO5+0TY5FuxaZ1HqP4DE9AFILVJPElLGR8Rp8hbZrfJsnyalJnvwXwv/HBQqKEybRy3F3rqAq4v4LF0LYNy+XCqQVFSVVWUHjvgeeeSnlOlml+qV2NE7oJue5rec8VjTewefFzrt93afe/XzVC7a379A10GVms6yWnZ284PX3D/GJOs1xivNtOANGu6pYjzoLWGl1opHBcv7Et0tYsFkLbNE4yUMpxWxG0MWit3RhWD8P7wCtcFtTTXfr1RHRTjYOhJtn/+1p54sBXp8Tr59yZ5eYx1vXwzGao8lJoN4IIKKXZ2tkh6PqOc4Yz3lxzvl1L27aIQC1gKCh04QiFdSxE/X/tnU+oHVcdxz/fmZu8iha0ViXUoql005WGIgWlS7XZRHdd2YXgRkEXLiLddKugC0EExUIVsRsVuxEUEVxZrZKmKSFt1IK1oVGEJL683MzM+bk4Z2bOnTtz730v8c087vnCfTP3zHnnfuc35/zO7/c7f0YKwed2//kDoeM6dOJVd4ylgHxkSq7aQ/Ag2MQyiLNY8P+rqmBvb48bN66zu3uN+a09XOUn48zyHGWQzbLwDLJGadfxgEz1bgsik7cmChyZZX4VXHiPn1coMFNGVrcfaxXTyp201tx37QLG8u7OTWhmJqq1Fofec9j8UyStZq5Bjys6UACLcarF8u4Ui+IyKueDl0OYiBIQx3d2wrd2zcDy1E5v+huOsiqCb2nkeUaW55jlLC46Mtaa0W2HF/L0xFNqU3UfWG0B9PQ8Gr72/4r4N0E4uuPbXgHs7t7g+rVr3Ly5i3MFx2Yzsp3c76fnHwbZsRAQA6TMz+GIhFhHph2G5NownQtunKqgBPzzziTyaLFAPd6/uH4gtlbqfLWsQqYFeQ3Pz/AKtw1S1nk3H6/vWBU9v7HspsS8Y35R090g8j9UE+qizIzKVZRlMX0l4IXR7tLSt4ijGeYI0WhJmKuoMMxyZtCU4cxYHiEegC1e6zPkVpqGA41yqPIsTPkNf5sq3YYvGtRWQTeouI7PRr5xk8VRv8qrjnjf2rvJ7n9vUBZzjs0ylN/jG37w/b2bRhMQiwgsxFla/xgsvDFIrt7nr1buvpW7XDgnpHy4DvTec3zfbZrZQayq5WdlzZ4GB1PAm8Up6mXR+yh3xW+ZOaqqoigKyrKYfkwAiJ1fQM0iIDOLWmbbBzhzQSGE4SoHspzM+cCARUUulB8nochtaLquTp5+JbIuCNV3fUkBqPMgu9qox2VYhwWTdqORA2sqjHMVVVVya37Lm/7mmM1mvvetg1+Zomm363j1Sc/3+r638labMz8cV1b4oOHM7+krcxA27WwJR7183GBVC3Oze17m2e82Dc/16JFvZEHQbdAb6I+NVMXQvJda1OYVgHMlRRi1cW71PPCJKYFQYZqexKJzOnZ6LWRHZYZV5nuQ3Pul5HlrdFlkSdhipa3L1cD3g2p/f0vL/7s0z3/Z7NhX+asUTR+XrlnqXBWG/IrQa5QU5W3vE+cZatYYL5pMm7slC5LtwCsEBOZKiqKiXhE1y/PGPRDq7KQTzw8J8ow3Hxlg0bXBYnMcWncgdi+GMCjnOo7RjSetUcgbWQAd13WRT7C0QtysKArm8znOubX3Mh0lELcMC8GWBTOTph4Go8BP9CL0Jq6issK/8EEzGj/L/GKShb6rti56DMwsKj/+6SW6B5wEAqwa5RrMezdCAcsKww/FlmVJ5QqclThXQhgWbIfmDvyLA+mR6V43EHNUJpy7DZaRv+MeXIgt1M9k+WmFRm9eAbSWCcuLoGzh0Mtn+epyvGA/MZlGvRhtnevrGGyD+jSQp40dGPVuT1VVMp/PKcqiGXpdRVt3tGjlLkHSv4Bd4N9jc9kn7idxPiwcRd5T4/whM3tfN3ESSgBA0ktm9ujYPPaDxPnwcBR5HxXOfe9iTkhI2CIkJZCQsOWYkhL4/tgEDoDE+fBwFHkfCc6TiQkkJCSMgylZAgkJCSNgdCUg6TOSLkm6LOns2HyGIOkNSa9IOifppZB2n6TfSHo9HN8zAZ7PSroq6UKU1stTHt8Jsj8v6dSEOD8j6Z9B3ucknY6ufT1wviTp0yNxflDS7yRdlPSqpK+E9EnLuhf1uvYxPkAO/BV4CDgOvAw8MianFVzfAO7vpH0TOBvOzwLfmADPx4FTwIV1PIHTwK/w01geA16cEOdngK/15H0k1JMd4GSoP/kInE8Ap8L5vcBrgdukZd33GdsS+Dhw2cz+Zma3geeBMyNz2g/OAM+F8+eAz47IBQAz+z3wn07yEM8zwI/M4w/AuyWdOBymLQY4D+EM8LyZzc3s78BlfD06VJjZFTP7Szi/AVwEHmDisu7D2ErgAeAf0fc3Q9oUYcCvJf1Z0hdD2gfM7Ar4SgG8fzR2qzHEc+ry/3IwnZ+NXK3JcZb0YeBjwIscQVmPrQR6p+UfOovN8AkzOwU8AXxJ0uNjE7oLmLL8vwd8BPgocAX4VkifFGdJ7wJ+BnzVzK6vytqTNglZj60E3gQejL5/EHhrJC4rYWZvheNV4Bd4E/Tt2qQLx6vjMVyJIZ6Tlb+ZvW1mlfmNDn5Aa/JPhrOkY3gF8BMz+3lIPnKyHlsJ/Al4WNJJSceBJ4EXRua0BEnvlHRvfQ58CriA5/pUyPYU8MtxGK7FEM8XgM+HyPVjwLXalB0bHX/5c3h5g+f8pKQdSSeBh4E/jsBPwA+Bi2b27ejSkZP16JFJfNT0NXyU9+mx+QxwfAgfkX4ZeLXmCbwX+C3wejjeNwGuP8WbzwW+9/nCEE+8ifrdIPtXgEcnxPnHgdN5fAM6EeV/OnC+BDwxEudP4s3588C58Dk9dVn3fdKMwYSELcfY7kBCQsLISEogIWHLkZRAQsKWIymBhIQtR1ICCQlbjqQEEhK2HEkJJCRsOZISSEjYcvwPAseEviEJ6E4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.imshow(x_test[4]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from model import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\Anaconda3\\envs\\environment\\lib\\site-packages\\keras_applications\\resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
      "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
     ]
    }
   ],
   "source": [
    "model_test = LeafCountingModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[17.377426]], dtype=float32)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_test.predict(np.expand_dims(x_ts[0], axis=0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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